Cooperative perception (CP) has emerged as an important research topic in connected and automated transportation systems, significantly enhancing situational awareness in challenging conditions such as limited communication bandwidth, harsh weather, and poor lighting. By sharing and aggregating data from multiple sources—including vehicles, roadside units (RSUs), and other infrastructure—CP addresses critical issues like occlusion and long-range perception in complex environments. This article provides a comprehensive review of existing CP methodologies, evaluating their effectiveness across diverse scenarios and fusion layers. We investigate approaches ranging from early to late collaboration strategies, highlighting how multimodal sensing at the network level can improve detection accuracy, system robustness, and adaptability. Additionally, we review publicly available datasets, identifying key gaps in their coverage of heterogeneous sensors, adverse conditions, and large-scale spatial scenarios. Finally, we propose actionable future research directions, including advanced algorithms for adverse environments, adaptive fusion strategies for heterogeneous sensors, and the integration of emerging technologies such as high-definition (HD) maps, large language models (LLMs), and end-to-end frameworks. These advancements aim to ensure reliability, interoperability, and scalability for real-world connected and automated vehicle (CAV) applications.

Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental understanding. This paper first formalizes multi-sensor fusion strategies into data-level, feature-level, and decision-level cate-gories and then provides a systematic review of deep learning-based methods corresponding to each strategy. We present key multi-modal datasets and discuss their applicability in ad-dressing real-world challenges, particularly in adverse weather conditions and complex urban environments. Additionally, we explore emerging trends, including the integration of Vision-Language Models (VLMs), Large Language Models (LLMs), and the role of sensor fusion in end-to-end autonomous driving, highlighting its potential to enhance system adaptability and robustness. Our work offers valuable insights into current meth-ods and future directions for multi-sensor fusion in autonomous driving.

Eco-approach and Departure (EAD) strategies optimize energy efficiency in Connected and Automated Vehicles (CAVs) by leveraging Signal Phase and Timing (SPaT) data to adjust trajectories. This paper extends the EAD strategy to incorporate actuated signals in a city-scale electrified network and introduces the Eco-Pedaling (EP) strategy to further reduce electricity consumption. First, we analyze off-peak and peak scenarios, applying eco-driving technologies to Connected and Automated Electric Vehicles (CAEVs) in the city network, assuming all vehicles are electric. Results show that CAEVs with eco-driving technologies achieve a 32.24% reduction in electricity consumption per mile during off-peak hours and 31.50% during peak hours, with a 15% CAEV penetration rate, corresponding to Toyota's market share in North America. Second, a 24-hour evaluation with the same CAEV penetration rate highlights a 6.02% reduction in total electricity consumption across the network, at the cost of a 3.58% decrease in network efficiency. Finally, analysis of varying CAEV penetration rates reveals that the system-level electricity consumption decreases as the proportion of CAEVs increases.

Interest in cooperative perception is growing quickly due to its remarkable performance in improving perception capabilities for connected and automated vehicles. This improvement is crucial, especially for automated driving scenarios in which perception performance is one of the main bottlenecks to the development of safety and efficiency. However, current cooperative perception methods typically assume that all collaborating vehicles have enough communication bandwidth to share all features with an identical spatial size, which is impractical for real-world scenarios. In this paper, we propose Adaptive Cooperative Perception, a new cooperative perception framework that is not limited by the aforementioned assumptions, aiming to enable cooperative perception under more realistic and challenging conditions. To support this, a novel feature encoder is proposed and named Pillar Attention Encoder. A pillar attention mechanism is designed to extract the feature data while considering its significance for the perception task. An adaptive feature filter is proposed to adjust the size of the feature data for sharing by considering the importance value of the feature. Experiments are conducted for cooperative object detection from multiple vehicle-based and infrastructure-based LiDAR sensors under various communication conditions. Results demonstrate that our method can successfully handle dynamic communication conditions and improve the mean Average Precision by 10.18% when compared with the state-of-the-art feature encoder.

Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation, which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. Although an unprecedented evolution is now happening in the area of computer vision for object perception, state-of-the-art perception methods are still struggling with sophisticated real-world traffic environments due to the inevitable physical occlusion and limited receptive field of single-vehicle systems. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) is born to unlock the bottleneck of perception for driving automation. In this paper, we comprehensively review and analyze the research progress on CP, and we propose a unified CP framework. The architectures and taxonomy of CP systems based on different types of sensors are reviewed to show a high-level description of the workflow and different structures for CP systems. The node structure, sensing modality, and fusion schemes are reviewed and analyzed with detailed explanations for CP. A Hierarchical Cooperative Perception (HCP) framework is proposed, followed by a review of existing open-source tools that support CP development. The discussion highlights the current opportunities, open challenges, and anticipated future trends.

A large number of Connected and Automated Vehicle (CAV) applications are being designed, developed, and deployed in order to greatly improve our transportation systems in terms of safety, mobility, and reducing environmental impacts. These benefits can be quantified by a variety of performance measures that are often cited in the literature. However, most of these CAV applications are typically designed to improve transportation systems only in a particular dimension, usually focusing on either safety, mobility, or the environment. Very few research papers have considered a wider range or combination of performance measures across multiple dimensions, examining potential co-benefits or tradeoffs between these measures. For example, you can design a CAV application that greatly improves safety, but it might come at the cost of reducing traffic throughput. Further, the design of the CAV applications is often static and limited to specific traffic scenarios and conditions. CAVs that can adapt to different conditions, and be “tunable” for different societal needs will have much greater impact and versatility. In this presentation, we examine various co-benefits and tradeoffs of current CAV applications and consider how we can design these systems to have greater flexibility when it comes to deployment. We cite not only different CAV applications evaluated in simulation, but also real-world CAV deployments that operate on various testbeds, such as the Innovation Corridor located in Riverside, California. Based on this analysis, we can consider several new research directions for future CAV deployments.

The emerging prevalence of electric vehicles (EVs) in shared mobility services has led to a groundbreaking trend for decarbonizing the shared mobility sector. However, it is still unclear how to maximize the efficiency of EVs to reduce greenhouse gas (GHG) emissions while maintaining high service quality, particularly considering the ongoing transition towards a fully electrified service fleet. In this paper, focusing on meal delivery, we proposed an eco-friendly on-demand meal delivery (ODMD) system to maximize the utilities of EVs to mitigate GHG emissions and maintain low operational cost and delay cost. The main feature of our system is that its fleet consists of electric and gasoline vehicles mirroring the evolving electrification trend in the shared delivery sector. A rolling horizon framework integrated with the adaptive large neighborhood search (RH-ALNS) algorithm was proposed to efficiently solve the meal order dispatching and routing problem with the mixed fleet. Three delivery policies were explored in the numerical study. Experiment results demonstrated that it is necessary for online meal delivery platforms to actively collect information of electric vehicles and take initiative to employ an eco-friendly delivery policy.

Exclusive bus lane strategy is widely adopted in many cities to improve bus operation efficiency and reliability. With the development of connected vehicle technologies, the dynamic bus lane (DBL) strategy was proposed, with allowing general vehicles to share use of the bus lane to improve traffic efficiency in general purpose lanes (GPLs). Previous studies have rarely considered the eco-driving strategy of connected and automated vehicles/buses (CAVs/CABs) in GPLs under the mixed traffic conditions, and how to ensure bus priority with DBL control. In this study, a novel DBL control strategy was developed under the partially connected vehicle environment. A trajectory planning method while considering the joint effects of bus stop and signal phase for CAB was adopted, an eco-driving strategy for CAVs in GPL was proposed using a trigonometry trajectory planning method. And a novel DBL control method was established by integrated trajectory planning for both the CAVs and CABs to ensure bus operation priority. Numerical experiments were conducted to evaluate performance of the proposed novel DBL control in terms of travel time and energy consumption of general vehicles at the different levels of CAV market penetration rates (MPRs). Results indicated that about 16%-42% energy savings can be achieved with MPR varying from 20% to 100%, and the travel time can be improved by about 4%-10%. Meanwhile, sensitivity analysis was conducted to quantify the impacts of key parameters, including vehicle target speeds, heterogeneous traffic flow, random arrival interval of cars, position of bus stop, traffic volume in GPLs and buffer length at the stop line of signalized intersections.

Understanding the behavior of human drivers and how they interact with other drivers is crucial to develop and improve the decision-making capabilities of connected and automated vehicles (CAVs). This allows CAVs to anticipate and proactively respond to the actions of other road users in a safe and efficient manner, especially in mixed-traffic environments. Most existing studies rely on neural networks to model such interaction implicitly, and very few studies attempt to interpret the interaction. Considering its interpretability and flexibility, the Granger causality (GC) framework is widely used to understand the relationships between different agents, as well as how these relationships change over time. In this paper, we integrate the knowledge of traffic and vehicle dynamics into the neural network to learn the Granger causality and explore vehicular interaction from multi-vehicle trajectories. The proposed algorithm has been validated using both the INTERACTION dataset and field data collected in Riverside, California. The results show that our algorithm is able to address three key questions regarding vehicular interaction: 1) whether the interactions exist between/among the vehicles; 2) when the interactions occur and terminate; and 3) how strong the interactions between/among vehicles are.

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience.

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a driver digital twin (DDT) for the online prediction of personalized lane-change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge–cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane-change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane-change intention can be recognized in 6 s on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 m within a 4-s prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched athttps://youtu.be/5cbsabgIOdM.

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of$0.69m$and$0.33m$for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of$3-4 Hz$.

In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven vehicles will share the road networks together. In such a mixed traffic environment, CAVs need to understand and predict maneuvers of surrounding vehicles for safer and more efficient interactions, especially when human drivers bring in a wide range of uncertainties. In this paper, we propose a learning-based lane-change prediction algorithm that considers the driving behaviors of the target human driver. To provide accurate maneuver prediction, we adopt a hierarchical structure that seamlessly seals both the lane-change decision prediction and the vehicle trajectory pre-diction together. Specifically, we propose a lane-change decision prediction method based on a Long-Short Term Memory (LSTM) network, and a trajectories prediction considering driver preference and vehicular interactions based on Inverse Reinforcement Learning (IRL). To validate the performance of the proposed methodology, a case study of an on-ramp merging scenario is conducted on a uniquely built human-in-the-loop simulation platform that can provide an immersive driving environment, collect data of lane-change behaviors, and test drivers' reactions to the prediction results in real time. It is shown in the simulation results that we can predict the lane-change decision 3 seconds before the vehicle crosses the line to another lane, and the Mean Euclidean Distance between the predicted trajectory and ground truth is 0.39 meters within a 4-second prediction window.

Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving automation applications based on expert systems or prede-fined control strategies are not in line with individual human driver's preference. To overcome this problem, we propose a Personalized Adaptive Cruise Control (P-ACC) system that can learn the driver's car-following preferences from historical data using model-based maximum entropy Inverse Reinforcement Learning (IRL). Once activated in real-time, the P-ACC system first classifies the driver type and the weather type (at that moment). The vehicle is then controlled using the pre-trained IRL model on the cloud of the associated class. The personalized IRL model on the cloud will be updated as more human driving data is collected from various scenarios. Numerical simulation with real-world naturalistic driving data shows that, the accuracy of reproducing the real-world driving profile improves up to 30.1% in terms of speed and 36.5% in terms of distance gap, when P-ACC is compared with the Intelligent Driver Model (IDM). Game engine-based human-in-the-loop simulation demonstrates that, the takeover frequency of the driver during the usage of P-ACC decreases up to 93.4%, compared with that during the usage of IDM-based ACC.

Object detection and tracking play a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems. Although current computer vision technologies can provide satisfactory object detection results in occlusion-free scenarios, the perception performance of onboard sensors is inevitably limited by the range and occlusion. Owing to the flexible location and pose for sensor installation, infrastructure-based detection, and tracking systems can enhance the perception capability of connected vehicles; as such, they have quickly become a popular research topic. In this survey paper, we review the research progress for infrastructure-based object detection and tracking systems. Architectures of roadside perception systems based on different types of sensors are reviewed to show a high-level description of the workflows for infrastructure-based perception systems. Roadside sensors and different perception methodologies are reviewed and analyzed with detailed literature to provide a low-level explanation for specific methods followed by Datasets and Simulators to draw an overall landscape of infrastructure-based object detection and tracking methods. We highlight current opportunities, open problems, and anticipated future trends.

Environment perception including detection, classification, tracking, and motion prediction are key enablers for automated driving systems and intelligent transportation applications. Fueled by the advances in sensing technologies and machine learning techniques, LiDAR-based sensing systems have become a promising solution. The current challenges of this solution are how to effectively combine different perception tasks into a single backbone and how to efficiently learn the spatiotemporal features directly from point cloud sequences. In this research, we propose a novel spatiotemporal attention network based on a transformer self-attention mechanism for joint semantic segmentation and motion prediction within a point cloud at the voxel level. The network is trained to simultaneously outputs the voxel level class and predicted motion by learning directly from a sequence of point cloud datasets. The proposed backbone includes both a temporal attention module (TAM) and a spatial attention module (SAM) to learn and extract the complex spatiotemporal features. This approach has been evaluated with the nuScenes dataset, and promising performance has been achieved.

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.

Ramp merging is considered as one of the most difficult driving scenarios due to the chaotic nature in both longitudinal and lateral driver behaviors (namely lack of effective coordination) in the merging area. In this study, we have designed a cooperative ramp merging system for connected vehicles, allowing merging vehicles to cooperate with others prior to arriving at the merging zone. Different from most of the existing studies that utilize dedicated short-range communication, we adopt a Digital Twin approach based on vehicle-to-cloud communication. On-board devices upload the data to the cloud server through the 4G/LTE cellular network. The server creates Digital Twins of vehicles and drivers whose parameters are synchronized in real time with their counterparts in the physical world, processes the data with the proposed models in the digital world, and sends advisory information back to the vehicles and drivers in the physical world. A real-world field implementation has been conducted in Riverside, California, with three passenger vehicles. The results show the proposed system addresses the issues of safety and environmental sustainability with an acceptable communication delay, compared to the baseline scenario where no advisory information is provided during the merging process.

Detailed road features like lane markers and stop bars are crucial for many recent Intelligent Transportation System (ITS) applications, especially for advanced driving assistant systems or autonomous vehicles. In this paper, a data-driven method is proposed to identify intersection areas and map stop bar positions without prior knowledge of road information. The proposed method includes 1) a novel and efficient approach to identify intersections by analyzing the entropy of vehicles’ moving directions; and 2) a statistical model for estimating the number, coordinates, and directions of stop bars by evaluating the upstream vehicles’ stopping locations. By applying the method to real-world vehicle positioning data collected at Ann Arbor, its applicability and robustness to handle data at an urban regional scale (a 1.2 km by 2 km rectangular area) are proven. The accuracy of intersection identification is 95.7% for trajectory covered regions. For stop bar positioning, the mean and standard deviation of the errors are 0.27 m and 0.32 m respectively, which satisfy most of the mobility and eco-driving connected and automated vehicle applications such as eco-approach and departure at signalized intersections.

Ramp merging is considered to be one of the major causes of traffic accidents and congestion due to its inherent chaotic nature. With the development of the connected and automated vehicle (CAV) technology, CAVs can conduct cooperative merging using communication, and can also handle complicated situations even with legacy vehicles. In this article, a game theory-based ramp merging strategy has been developed for the optimal merging coordination of CAVs in mixed traffic, which can determine the dynamic merging sequence and corresponding longitudinal/lateral control. This strategy improves the safety and efficiency of the merging process by ensuring a safe intervehicle distance and harmonizing the speeds of CAVs in the traffic stream. To verify the proposed strategy, mixed traffic simulation runs under different penetration rates and different congestion levels have been carried out on an innovative Unity-SUMO integrated platform, which connects a game engine-based driving simulator with a state-of-the-art microscopic traffic simulator. The results show that the average speed of traffic flow can be increased up to 210%, while the fuel consumption can be reduced up to 53.9%. In addition, the driving volatility can be stabilized to a level with 0% extreme values.

Our current transportation system suffers from a number of problems in terms of safety, mobility, and environmental sustainability. The emergence of innovative intelligent transportation systems (ITS) technologies, and in particular connected and automated vehicles (CAVs), provides many opportunities to address the aforementioned issues. In this paper, we propose a hierarchical ramp merging system that not only generates microscopic cooperative maneuvers for CAVs on the ramp to merge into the mainline traffic flow, but also provides controllability of the ramp inflow rate, thereby enabling macroscopic traffic flow control. A centralized optimal control-based approach is proposed to smooth the merging flow, improve the system-wide mobility, and decrease the overall fuel consumption of the network. Linear quadratic trackers in both finite horizon and receding horizon forms are developed to solve the optimization problem in terms of path planning and sequence determination, where a microscopic vehicle fuel consumption model is applied. Extensive traffic simulation runs have been conducted using PTV VISSIM to evaluate the impact of the proposed system on a segment of SR-91 E in Corona, California. The results confirm that under the regulated inflow rate, the proposed system can avoid potential traffic congestion and improve mobility (e.g., VMT/VHT) up to 147%, with a 47% fuel savings compared to the conventional ramp metering and the ramp without any control approach.

Digital twin, an emerging representation of cyberphysical systems, has attracted increasing attentions very recently. It opens the way to real-time monitoring and synchronization of real-world activities with the virtual counterparts. In this study, we develop a digital twin paradigm using an advanced driver assistance system (ADAS) for connected vehicles. By leveraging vehicle-to-cloud (V2C) communication, on-board devices can upload the data to the server through cellular network. The server creates a virtual world based on the received data, processes them with the proposed models, and sends them back to the connected vehicles. Drivers can benefit from this V2C based ADAS, even if all computations are conducted on the cloud. The cooperative ramp merging case study is conducted, and the field implementation results show the proposed digital twin framework can benefit the transportation systems regarding mobility and environmental sustainability with acceptable communication delays and packet losses.

From November 4th to 7th, 2018, the IEEE Intelligent Transportation Systems Society (ITSS) sponsored the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018), one of the most prestigious academic meetings on Intelligent Transportation Systems. The 2018 annual flagship conference of the IEEE ITS Society was held in Maui, Hawaii, reaching unprecedented numbers in terms of both submissions and presented papers.

The emergence of connected and automated vehicle (CAV) technology has the potential to bring a number of benefits to our existing transportation systems. Specifically, when CAVs travel along an arterial corridor with signalized intersections, they can not only be driven automatically using pre-designed control models but can also communicate with other CAVs and the roadside infrastructure. In this paper, we describe a cooperative eco-driving (CED) system targeted for signalized corridors, focusing on how the penetration rate of CAVs affects the energy efficiency of the traffic network. In particular, we propose a role transition protocol for CAVs to switch between a leader and following vehicles in a string. Longitudinal control models are developed for conventional vehicles in the network and for different CAVs based on their roles and distances to intersections. A microscopic traffic simulation evaluation has been conducted using PTV VISSIM with realistic traffic data collected for the City of Riverside, CA, USA. The effects on traffic mobility are evaluated, and the environmental benefits are analyzed by the U.S. Environmental Protection Agency's MOtor Vehicle Emission Simulator (MOVES) model. The simulation results indicate that the energy consumption and pollutant emissions of the proposed system decrease, as the penetration rate of CAVs increases. Specifically, more than 7% reduction on energy consumption and up to 59% reduction on pollutant emission can be achieved when all vehicles in the proposed system are CAVs.

In[1], the traffic volume of the left-turn movement was set to be extremely high (with significant queue spill back from the left-turn bay) in the microscopic traffic simulator. Although some explanation has been provided in the paper, we consider the simulation results to be non-representative. Therefore, we reran the simulation with more balanced values across different movements, and updated the simulation results inTABLE Von page 8 of[1]with the one shown below.

The eco-approach and departure (EAD) application for signalized intersections has been proved to be environmentally efficient in a Connected and Automated Vehicles (CAVs) system. The traffic and signal phase and timing (SPaT) information transmitted from the roadside equipment unit, vehicle equipped sensors (e.g. radars) and other connected vehicles are the main inputs to the existing algorithms. However, due to the limitation of the communication and sensing range, it is too late to start eco-driving until preceding traffic is fully detected. Instead, the historical data, such as queue length distribution may be applied to developing a robust speed profile that enables eco-driving to start in an early stage. In this paper, a two-phase iterative approach is developed with the use of historical queue distribution. A graph-based model is created with nodes representing states of the host vehicle and traffic condition, and directed edges with weight representing expected energy consumption between two connected states. The shortest path is calculated that minimizes the total energy consumption for vehicles approaching a pre-timed signalized intersection. Numerical simulations have shown that the proposed method is robust and adaptive to varying traffic and queue conditions, and could achieve around 9% energy savings compared to other baseline methods.

The coordinated on-ramp merging is one of the most common but critical vehicular applications that require complex data transmission and low-latency communication in the Connected and Automated Vehicles (CAVs) environment. An effective way to address on-ramp merging is to leverage the edge computing to optimize the coordination among vehicles to achieve overall minimum vehicle travel time and energy consumption. In this study, we propose an Bi-level Optimal Edge Computing (BOEC) model for on-ramp merging in the CAVs environment to optimize both merge time and vehicle trajectory. The simulation results show that the proposed BOEC model achieves great benefits in vehicle mobility, energy saving and air pollutant emission reduction by providing an energy-efficient trajectory following the optimal merge time without compromising safety.

Activities of transit buses traveling along arterial roads and city streets consist of frequent stops and idling events at many predictable occasions, e.g., loading/unloading passengers at bus stops, approaching traffic signals or stop signs, and going through recurrent traffic congestion, etc. Besides designing transit buses with electric powertrain systems that can save a noticeable amount of energy thanks to regenerative braking, this urban traffic environment also unfolds a number of opportunities to further improve their energy efficiency via vehicle connectivity and autonomy. Therefore, this paper proposes a complete and novel simulation framework of integrated vehicle/powertrain eco-operation system for electric buses (Eco-bus) by co-optimizing the vehicle dynamics and powertrain (VD&PT) controls. A comprehensive evaluation of the proposed system on mobility benefits and energy savings has been conducted over various traffic conditions. Simulation results are presented to showcase the superiority of the proposed simulation framework of the Eco-bus compared to the conventional bus, particularly in terms of mobility and energy efficiency aspects.

The connected vehicle eco-approach and departure (EAD) application for signalized intersections has been widely studied and is deemed to be effective in terms of reducing energy consumption and both greenhouse gas and other criteria pollutant emissions. Prior studies have shown that tangible environmental benefits can be gained by communicating the driver with the signal phase and timing (SPaT) information of the upcoming traffic signals with fixed time control to the driver. However, similar applications to actuated signals pose a significant challenge due to their randomness to some extent caused by vehicle actuation. Based on the framework previously developed by the authors, a real-world testing has been conducted along the El Camino Real corridor in Palo Alto, CA, USA, to evaluate the system performance in terms of energy savings and emissions reduction. Strategies and algorithms are designed to be adaptive to the dynamic uncertainty for actuated signal and real-world traffic. It turns out that the proposed EAD system can save 6% energy for the trip segments when activated within DSRC ranges and 2% energy for all trips. The proposed system can also reduce 7% of CO, 18% of HC, and 13% of NOx for all trips. Those results are compatible with the simulation results and validate the previously developed EAD framework.

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation's dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.

Connected vehicle (CV) technology has great potential to improve the performance of today's advanced driver assistance systems in terms of safety, energy efficiency, and driving comfort. The aim of this paper is to develop a specific CV application that assists with lane selection, i.e., finding the best travel lane in terms of travel time based on predicted lane-level traffic states. In this paper, a spatial-temporal model (ST-model) was developed, which utilizes spatial and temporal information of road cells to predict future traffic states. This information was used by the proposed lane selection assistance application to select an optimal lane sequence for the application-equipped vehicle. A comprehensive simulation-based evaluation was then conducted under various scenarios, e.g., with different traffic volumes, penetration rates of communication-capable vehicles, and information update cycles. The evaluation results reveal several interesting findings, including: 1) the proposed ST-model outperforms the basic estimation model in terms of traffic state prediction accuracy; 2) travel times of application-equipped vehicles can be reduced by up to 8% with the use of the proposed lane selection assistance application when compared with the baseline, under various traffic scenarios; 3) the application can be effective in the early deployment stage of CV technology, where the penetration rate of communication-capable vehicles is still low; and 4) the potential conflict risk of application-equipped vehicles is reduced, although the application is mainly designed for mobility benefits, due to the more strategic and informed lane changes suggested by the proposed application.

Using connected vehicle technology, a number of eco-approach and departure (EAD) strategies have been designed to guide vehicles through signalized intersections in an eco-friendly way. Most of the existing EAD applications have been developed and tested in traffic-free scenarios or in a fully connected environment, where the presence and behavior of all surrounding vehicles are detectable. In this paper, we describe a prediction-based EAD strategy that can be applied toward more realistic scenarios, where the surrounding vehicles can be either a connected or non-connected. Unlike highway scenarios, predicting speed trajectories along signalized corridors is much more challenging due to disturbances from signals, traffic queues, and pedestrians. Based on vehicle activity data available via inter-vehicle communication or onboard sensing (e.g., by radar), we evaluate three state-of-the-art nonlinear regression models to perform short-term speed forecasting of the preceding vehicle. It turns out radial basis function neural network outperformed both Gaussian process and multi-layer perceptron network in terms of prediction accuracy and computational efficiency. Using signal phase and timing information and the predicted state of the preceding vehicle, our prediction-based EAD algorithm achieved better fuel economy and emissions reduction in urban traffic and queues at intersections. Results from the numerical simulation using the next generation simulation data set show that the proposed prediction-based EAD system achieve 4.0% energy savings and 4.0% - 41.7% pollutant emission reduction compared with a conventional car following strategy. Prediction-based EAD saves 1.9% energy and reduces criteria pollutant emissions by 1.9% - 33.4% compared with an existing EAD algorithm without prediction in urban traffic.

In recent years, eco-friendly driving or ecodriving technologies are being developed to assist human drivers to achieve maximum fuel/energy efficiency in different driving conditions. Enhanced by V2X wireless communications, connected ecodriving is expected to be very promising in reducing transportation-related fossil fuel consumption as well as pollutant emissions. Besides, the deployment of electric vehicles (EVs) also has great potential in reducing greenhouse gas emissions due to the use of batteries as the sole energy source. Although recent research shows that significant energy savings can be achieved with the aid of ecodriving systems in real-world driving, there have been very few research efforts that consider human driver error, especially for electric vehicle (EV) driving. In this paper, a connected cooperative ecodriving system for energy-efficient driving that considers human driver error is designed and evaluated with an EV energy consumption model. Real-world driving data were collected and used to evaluate system performance in terms of energy consumption. The simulation and numerical analysis shows that an average of 12% energy savings can be achieved by the proposed system that considers human driver error comparing with the conventional ecodriving system without considering driver error.

To mitigate air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies just simple follow predefined rules that are not adaptive to changing driving conditions; other strategies as starting to incorporate accurate prediction of future traffic conditions. In this study, a deep reinforcement learning based PHEV energy management system is designed to autonomously learn the optimal fuel use from its own historical driving record. It is a fully data-driven and learning-enabled model that does not rely on any prediction or predefined rules. The experiment results show that the proposed model is able to achieve 16.3% energy savings comparing to conventional binary control strategies.

A large number of Connected and Automated Vehicle (CAV) applications have been emerging that benefit transportation systems in terms of safety, mobility and the environment. These benefits can be quantified by a variety of performance indices (PIs) as described in recent literature. However, there has been very little research in analyzing the potential co-benefits and tradeoffs among all these PIs for the various CAV applications. In this paper, we examine a number of CAV applications whose system effectiveness focus is targeted on the three key areas of safety, mobility and environment, and then examine whether some PIs are synergistic or antagonistic. Using the Lane Speed Monitoring application as a specific example, we explore the in-depth relationship between different types of measures of effectiveness (MOEs) under different penetration rates of the technology, in order to show the association between the application focus and tradeoffs to be made among different performance measures. As part of the analysis, several future research directions are discussed, including the identification of key influential factors on system performance to obtain co-benefits in terms of different types of MOEs.

Connected and automated vehicle (CAV) technology has become increasingly popular. As an example, Cooperative Adaptive Cruise Control (CACC) systems are of high interest, allowing CAVs to communicate and cooperate with each other to form platoons, where one vehicle follows another with a predefined spacing or time gap. Although numerous studies have been conducted on CACC systems, very few have examined the protocols from the perspective of environmental sustainability, not to mention from a platoon-wide consideration. In this study, we propose a vehicle-to-vehicle (V2V) communication based Eco-CACC system, aiming to minimize the platoon-wide energy consumption and pollutant emissions at different stages of the CACC operation. A full spectrum of environmentally-friendly CACC maneuvers are explored and the associated protocols are developed, including sequence determination, gap closing and opening, platoon cruising with gap regulation, and platoon joining and splitting. Simulation studies of different scenarios are conducted using MATLAB/Simulink. Compared to an existing CACC system, the proposed one can achieve additional 2% energy savings and additional 17% pollutant emissions reductions during the platoon joining scenario.

Probe vehicles that measure position and speed have emerged as a promising tool for traffic data collection and performance measurement, but the sampling rates of most probe vehicle sensor data available today are low (ranging from 10 to 60 s per sample), and the data coverage is limited. Therefore, it is challenging to accurately estimate the vehicle dynamic states in both space and time based on these sparse mobile sensor data. In this paper, a stochastic model is proposed to estimate the second-by-second vehicle speed trajectories by examining all possible sequences of modal activities (i.e., acceleration, deceleration, cruising, and idling) between consecutive data points from sparse position and speed measurements. The likelihood of occurrence of each sequential pattern is first quantified by mode-specific a priori distributions. The vehicle dynamic state probability is then formulated as the product of probabilities for multiple independent events. Therefore, a detailed vehicle speed trajectory can be reconstructed using the optimal modal activity sequence, which maximizes the likelihood. The proposed model is calibrated and validated using the Next-Generation SIMulation dataset. The results show the substantial improvements on the accuracy of estimated vehicle trajectories compared with a baseline method based on linear interpolation. The proposed model is applied to a large-scale vehicle activity dataset to demonstrate the estimation of hourly traffic delay variation.

Plug-in hybrid electric vehicles (PHEVs) have been regarded as one of several promising countermeasures to transportation-related energy use and air quality issues. Compared with conventional hybrid electric vehicles, developing an energy management system (EMS) for PHEVs is more challenging due to their more complex powertrain. In this paper, we propose a generic framework of online EMS for PHEVs that is based on an evolutionary algorithm. It includes several control strategies for managing battery state-of-charge (SOC). Extensive simulation testing and evaluation using real-world traffic data indicates that the different SOC control strategies of the proposed online EMS all outperform the conventional control strategy. Out of all the SOC control strategies, the self-adaptive one is the most adaptive to real-time traffic conditions and the most robust to the uncertainties in recharging opportunity. A comparison to the existing models also employing short-term prediction shows that the proposed model can achieve the best fuel economy improvement but requiring less trip information.

Safety, mobility and environmental sustainability represent three cornerstones when evaluating the effectiveness of an intelligent transportation system. However, very few studies have conducted a holistic performance assessment for a connected vehicle (CV) based application. In this study, an environment-focused CV application, called Eco-Approach and Departure (EAD) application at signalized intersections, is used as an example. Its safety, mobility and environmental sustainability parameters are carefully evaluated through comprehensive simulation analyses over a real-word network. A variety of scenarios have been tested and the impact analysis is conducted from two perspectives: 1) EAD-equipped vehicles vs. non-equipped vehicles; and 2) overall traffic. The results indicate that the benefits of mobility and environmental sustainability show more consistent patterns across different scenario while safety impacts are more scenario-dependent.

Automatic longitudinal control of vehicles is an automobile technology that has been implemented for many years. Connected eco-driving has the potential to extend the capability of an automatic longitudinal control by minimizing the energy consumption and emissions of the vehicle. In this paper, we propose a power-based longitudinal control algorithm for a connected eco-driving system, which takes into account the vehicle's brake specific fuel consumption or BSFC map, roadway grade, and other constraints (e.g., traffic condition ahead and traffic signal status of the upcoming intersection) in the calculation of an optimal speed profile in terms of energy savings and emissions reduction. The performance of the proposed algorithm was evaluated through extensive numerical analyses of driving along a signalized arterial, and the results validated the effectiveness of the proposed algorithm as compared with baseline and an existing eco-driving algorithm.

The Eco-Approach and Departure application for fixed-time traffic signals, which uses the signal phase and timing information from the upcoming traffic signal to better guide a driver through the intersection in an environmentally-friendly way, has shown promising results in terms of fuel savings and carbon emissions reduction. However, there is very limited research on the development and evaluation of such application for actuated traffic signals. This paper proposes a framework for the Eco-Approach and Departure application for actuated signals which takes into account uncertainties in count-down information, preceding vehicle's state, and potential driver's distraction issues. The framework has been evaluated with numerical experiments. The results indicated that the proposed framework is effective at reducing energy consumption and emissions of the equipped vehicle, especially when the initial entry speed is relatively low.

The energy management system (EMS) is crucial to a plug-in hybrid electric vehicle (PHEV) in reducing its fuel consumption and pollutant emissions. The EMS determines how energy flows in a hybrid powertrain should be managed in response to a variety of driving conditions. In the development of EMS, the battery state-of-charge (SOC) control strategy plays a critical role. This paper proposes a novel evolutionary algorithm (EA)-based EMS with self-adaptive SOC control strategy for PHEVs, which can achieve the optimal fuel efficiency without trip length (by time) information. Numerical studies show that this proposed system can save up to 13% fuel, compared to other on-line EMS with different SOC control strategies. Further analysis indicates that the proposed system is less sensitive to the errors in predicting propulsion power in real-time, which is favorable for on-line implementation.

To better regulate traffic flow and reduce the potential impacts due to uncoordinated lane changes, we proposed a real-time optimal lane selection (OLS) algorithm by using the information available from connected vehicle (CV) technology. Such information includes the location, speed, lane and desired driving speed of individual vehicle agents (VA) on a localized roadway. Microscopic traffic simulation studies show that the proposed algorithm can result in both mobility and environmental benefits for the entire traffic system. Specifically, the application of the OLS algorithm reduces the average travel time by up to 3.8% and the fuel consumption by around 2.2%. In addition, the reduction in emissions of criteria pollutants, such as CO, HC, NOx and PM2.5 ranges from 1% to 19%, depending on the congestion level of the roadway segment. Potential extensions of the proposed OLS algorithm are discussed at the end of this paper.

This paper introduces a multi-agent systems (MAS) based freight signal priority algorithm with the stated goal of reducing network-wide energy and emissions. The proposed infrastructure and communication protocol is highly flexible in that it can be applied to multiple measures of effectiveness (MOEs) such as energy, emissions, travel delay, or any combination. Furthermore, the adaptive nature of the optimized signal control ensures extension to multiple arterial intersections. The proposed algorithm has been implemented and evaluated on an isolated intersection in the microscopic simulation environment. The results indicate that the application of the proposed Eco-Friendly Freight Signal Priority algorithm improves upon traditional traffic signal priority by providing fuel and travel time savings to both freight and non-freight traffic.

There has been significant interest in plug-in hybrid electric vehicles (PHEVs) as a means to decrease dependence on imported oil and to reduce greenhouse gases as well as other pollutant emissions. One of the critical considerations in PHEV development is the design of its energy-management strategy, which determines how energy in a hybrid powertrain should be produced and utilized as a function of various vehicle parameters. In this paper, we propose an intelligent energy-management strategy for PHEVs. At the trip level, the strategy takes into account a priori knowledge of vehicle location, roadway characteristics, and real-time traffic conditions on the travel route from intelligent transportation system technologies in generating a synthesized velocity trajectory for the trip. The synthesized velocity trajectory is then used to determine battery's charge-depleting control that is formulated as a mixed-integer linear programming problem to minimize the total trip fuel consumption. The strategy can be extended to optimize vehicle fuel consumption at the tour level if a preplanned travel itinerary for the tour and the information about available battery recharging opportunities at intermediate stops along the tour are available. The effectiveness of the proposed strategy, both for the trip- and tour-based controls, was evaluated against the existing binary-mode energy-management strategy using real-world trip/tour examples in southern California. The evaluation results show that the fuel savings of the proposed strategy over the binary-mode strategy are around 10%-15%.

Intelligent Transportation System (ITS) technology is often aimed at improving vehicle safety and mobility. Lately a number of ITS applications are also focusing on environmental issues such as reducing greenhouse gases (through improved fuel economy) and reducing overall pollutant emissions. Typical environmental ITS applications (e.g., eco-routing) focus on reducing total mass vehicle emissions for generalized areas. To date however, environmental ITS applications haven't considered emissions from a pollutant exposure point-of-view. In this paper, we introduce a new vehicle routing methodology that goes beyond minimizing overall pollutant emissions, instead minimizing pollutant exposure to localized populations along roadways. As part of this effort, a unique modeling suite has been developed to allow for the evaluation of environmental ITS applications from a traffic emissions exposure point of view. For the routing algorithm, the human intake fraction that is commonly used for quantifying emission exposure is modeled and used as a routing cost. Experimental modeling results show that the intake fraction of particulate matter for 5-14 year-old school children on school days can be reduced approximately 80%-90% on a typical schoolday with the implementation of intelligent routing algorithms.

Transportation is responsible for approximately a third of greenhouse gases (GHG) and a major source of other pollutants including hydrocarbons (HC), carbon monoxide (CO), and oxides of nitrogen (NOx). Intelligent Transportation System (ITS) technology can be used to lower vehicle emissions and fuel consumption, in addition to reducing traffic congestion, smoothing traffic flow, and improving roadway safety. As wireless communication advances, connected-vehicles-based Advanced Traffic Management Systems (ATMS) have gained significant research interest due to their high potential. In this study, we examine the concept of ATMS for connected vehicles using a multi-agent systems approach, where both vehicle agents and an intersection management agent can take advantage of real-time traffic information exchange. This dynamic strategy allows an intersection management agent to receive state information from vehicle agents, reserve the associated intersection time-space occupancies, and then provide feedback to the vehicles. The vehicle agents then adjust their trajectories to meet their assigned time slot. Based on preliminary simulation experiments, the proposed strategy can significantly reduce fuel consumption and vehicle emissions compared to traditional signal control systems.

Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin–destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.

The ability to monitor the state of a given roadway in order to better manage traffic congestion has become increasingly important. Sophisticated traffic management systems able to process both the static and mobile sensor data and provide traffic information for the roadway network are under development. In addition to typical traffic data such as flow, density, and average traffic speed, there is now strong interest in environmental factors such as greenhouse gases, pollutant emissions, and fuel consumption. It is now possible to combine high-resolution real-time traffic data with instantaneous emission models to estimate these environmental measures in real time. In this paper, a system that estimates average traffic fuel economy, CO2, CO, HC, and NOxemissions using a computer-vision-based methodology in combination with vehicle-specific power-based energy and emission models is presented. The CalSentry system provides not only typical traffic measures but also gives individual vehicle trajectories (instantaneous dynamics) and recognizes vehicle categories, which are used in the emission models to predict environmental parameters. This estimation process provides far more dynamic and accurate environmental information compared with static emission inventory estimation models.

Many intelligent transportation system (ITS) applications will increasingly rely on lane-level vehicle positioning that requires high accuracy, bandwidth, availability, and integrity. Lane-level positioning methods must reliably work in real time in a wide range of environments, spanning rural to urban areas. Traditional positioning sensors such as the Global Navigation Satellite Systems may have poor performance in dense urban areas, where obstacles block satellite signals. This paper presents a sensor fusion technique that uses computer vision and differential pseudorange Global Positioning System (DGPS) measurements to aid an inertial navigation system (INS) in challenging environments where GPS signals are limited and/or unreliable. To supplement limited DGPS measurements, this method uses mapped landmarks that were measured through a priori observations (e.g., traffic light location data), taking advantage of existing infrastructure that is abundant within suburban/urban environments. For example, traffic lights are easily detected by color vision sensors in both day and night conditions. A tightly coupled estimation process is employed to use observables from satellite signals and known feature observables from a camera to correct an INS that is formulated as an extended Kalman filter. A traffic light detection method is also outlined, where the projected feature uncertainty ellipse is utilized to perform data association between a predicted feature and a set of detected features. Real-time experimental results from real-world settings are presented to validate the proposed localization method.

As traffic surveillance technology continues to grow worldwide, computer vision-based vehicle tracking is becoming increasing important. One of the key challenges with vehicle tracking is dealing with high density traffic, where occlusion often leads to foreground splitting and merging errors. In order to help solve this problem, global features such as color or local features like corners can be used for tracking. However, tracking based on global features or local features alone does not work well with a high amount of occlusion. In this paper, we propose a real-time multi-vehicle tracking approach, which combines both local feature tracking and a global color probability model. In cases with low occlusion, corner feature detection and tracking algorithm can be used to estimate vehicle positions and trajectories. When there is a high degree of occlusion, corner features can be tracked to provide position estimates of moving objects. Then a color probability can be calculated in the occluded area to determine which object each pixel belongs to. This approach is scalable to both stationary surveillance video and moving camera video. Experimental results from a challenging transportation video clip are presented.

The majority of today's automated vehicle location (AVL) systems use Global Positioning System (GPS) technology, which can provide position information with an accuracy of approximately 15 m. Recently, low-cost Differential GPS (DGPS) receivers, which have a positioning accuracy of approximate 2-3 m, have become available. With this increased accuracy, it is now possible to perform AVL down to specific roadway lanes. In this paper, a vehicle-lane-determining system is described, consisting of an onboard DGPS receiver that is connected with a wireless communications channel, a unique lane-level digital roadway database, a developed lane-matching algorithm, and a real-time vehicle location display. Lane-level positioning opens up the door for a number of new intelligent transportation system applications such as better fleet management, lane-based traffic measurements from probe vehicles, and lane-level navigation. The developed low-cost system has been tested on a number of roadways and has performed very well when used with accurately surveyed map data. Based on more than 100 000 s, it has correctly determined the lane 97% of the time.

Development of multiple robot systems which solve complex and dynamic problems in parallel and distributed manners is one of the key issues in robotics research. The multiple robot systems require robust methods to identify robots for collaborative behaviors. This paper proposes a method using omnidirectional vision sensors for the identification between the robots. In addition to the several advantages of the omnidirectional vision sensor as a vision of a mobile robot, the omnidirectional vision sensor brings a significant benefit for realizing collaborative behaviors in multiple robot systems. After discussing on the algorithm, this paper shows several simulation results and real experimental results in a real environment.

Mobile robots often require a full 360/spl deg/ view of their environment in order to perform navigational tasks such identifying landmarks, localizing within the environment, and determining free paths in which to move. In the past few years, several research efforts have focused on obtaining panoramic views (i.e. 360/spl deg/ images) around the robot using wide angle lenses, spherical or conic mirrors, or rotating a camera while imaging. Although the wide angle lens and mirror techniques can provide rapid image acquisition, they lack high azimuth angle resolution required by many mobile robot navigational tasks. Panning (i.e. rotating) a camera on the other hand can provide high azimuth angle resolution, but the process requires a long time to obtain a panoramic view. We introduce a new system that can acquire panoramic images quickly, using the camera panning technique. The system makes use of a fast line scan camera, instead of a slower, conventional area scan camera. In addition, we have developed a coarse-to-fine panoramic imaging technique that is based on smart sensing principles. Using the active vision paradigm, we control the motion of the rotating camera using feedback from the images. This results in high acquisition speeds and proportionally low storage requirements. Preliminary experimentation has been carried out, and results we given.

We present a computationally inexpensive method that rapidly and robustly determines both the translational direction and rotational component of motion through the use of an active vision sensor. The method employs an intelligent gaze control strategy where an active camera first fixates on an item in the environment while simultaneously measuring motion parallax. The camera then rapidly saccades to a different fixation point, based on this measure. The algorithm iteratively seeks out fixation points that are closer to the translational direction of motion, rapidly converging so that the camera always points in the instantaneous direction of motion. At that point, the tracking motion of the camera is equal but opposite in sign to the mobile entity's rotational component of motion.>

The authors present two computationally inexpensive methods that rapidly and robustly determine both the translational and the rotational components of robot motion through the use of an active camera. Two cases are considered: the first where the instantaneous direction of motion is known, and the second when the instantaneous direction of motion is unknown. Based on the animate vision paradigm, gaze control is used to allow the robot's camera to fix on items on the environment, and then rapidly move on to other fixation points. The algorithms iteratively seek out fixation points that lie in the forward direction, converging so that the active camera is aligned along the translational direction of motion. At that point, the tracking motion of the camera is equal but opposite in sign to the robot's rotational component of motion. Experiments were carried out both in simulation and in the real world, giving results that are close to the actual motion parameters of the robot.>

The authors introduce an approach to building a qualitative description of scenes along a route, which is used in route recognition by a mobile robot. The description consists of a series of landmarks autonomously selected by the robot from a generalized panoramic view generated as a visual memory of scenes along routes. The basic idea to bridge the quantitative panoramic view to qualitative landmarks is to examine the distinctiveness of patterns in the image and select landmarks from unique patterns that are remarkable by which to navigate.>

This paper introduces an approach to build a qualitative description of scenes along a route, which is used in route recognition by a mobile robot. The description consists of a series of landmarks autonomously selected by the robot from a panoramic view, which has been generated as a visual memory of scenes along routes. The basic idea to bridge the quantitative panoramic view to qualitative landmarks is to examine the 'distinctiveness' of patterns in the image and select landmarks from unique patterns that are remarkably by which to navigate.>

We present a new method of automated inspection of microelectronic structures. The method is based on color rather than black and white vision, and is a first such application of color vision to inspection for microelectronics fabrication. We describe the general method and demonstrate its practical implementation in the measurement of oxide thicknesses. A key result of this work is to have established sensitivity criteria for color detection in microelectronic structures. The resolution achieved allows us to measure differences in oxide thickness to approximately 30 Angstroms or better. By using the Ohta coordinates, our system can discrimimate between cyclically repeating hues. This determination can be done very rapidly (approximately 100 milliseconds) and does not require a complex (and thus expensive) computer system. An additional advantage of our method is the possibility of more easily and flexibly performing oxide thickness measurements in situ, than can be accomplished with standard techniques, such as ellipsometry.