Low latency in establishing a communication link is a key to maximize the performance of vehicle-to-vehicle wireless LANs (V2V WLANs), as contact duration of vehicles is often limited due to their fast mobility. However, previous works have mainly addressed Vehicle-to-Infrastructure (V2I) link setup latency on legacy Wi-Fi standards (IEEE 802.11b/g). Moreover, there has been a lack of performance evaluation regarding V2V link setup latency with current Wi-Fi technology and IEEE 802.11ai standard defines Fast Initial Link Setup (FILS), which simplifies the link setup process to reduce the latency. In this paper, we investigate the characteristics of V2V link setup latency with regular Extensible Authentication Protocol (EAP) authentication (EAP-TLS) and FILS with current Wi-Fi technology. The evaluation was conducted in general vehicular scenarios with different combinations of inter-vehicle distance, vehicle speed and type of roads (i.e., urban roads vs highways). FILS achieved the average link setup latency of 0.32 seconds, outperforming EAP-TLS that resulted in the average latency of 2.35 seconds. The link setup latency was stable in most of the scenarios we tested regardless of inter-vehicle distance, speed and Wi-Fi signal strength. The only exception was on highways, where significantly longer link setup latency was observed when the relative speed and distance changed rapidly, line-of-sight (LoS) was fully blocked by other vehicles and there were no other objects (e.g., buildings and vegetation) that form indirect signal propagation paths.

3D visual perception tasks, such as 3D detection from multi-camera images, are essential components of autonomous driving and assistance systems. However, designing computationally efficient methods remains a significant challenge. In this paper, we propose a Mamba-based framework called MamBEV, which learns unified Bird's Eye View (BEV) representations using linear spatio-temporal SSM-based attention. This approach supports multiple 3D perception tasks with significantly improved computational and memory efficiency. Furthermore, we introduce SSM based cross-attention, analogous to standard cross attention, where BEV query representations can interact with relevant image features. Extensive experiments demonstrate MamBEV's promising performance across diverse visual perception metrics, highlighting its advantages in input scaling efficiency compared to existing benchmark models.

Multi-view camera-based 3D object detection through unified Bird's Eye View (BEV) representation has become popular for autonomous driving due to its low cost, but efficiently inferring precise spatial and temporal information from cameras alone remains a significant challenge. Transformer-based approaches have shown substantial performance improvements but have the drawback of quadratic memory complexity — making these architectures ill-suited for edge deployment. Recently, State Space Models (SSMs) offer a more favorable balance of computational efficiency and performance in 2D vision, suggesting that they could help here as well. We present TinyBEV, an efficient BEV framework for multi-view 3D perception. For spatial modeling, we replace cross attention with SSMs that fusing BEV and camera images with linear complexity. For temporal modeling, we adopt a lightweight, linear-complexity history-fusion scheme that uses explicit time conditioning and channel-level aggregation instead of cross-frame attention. Both fusion strategies follow small constant scaling with respect to history length and enabling edge-friendly deployment. Experiments on NuScenes datasets demonstrate that TinyBEV is comparable with other state-of-the-art methods across diverse visual perception metrics with advantages in computational efficiency.

Modern cars monitor their surroundings and record video to deter intruders, but current surveillance systems operate independently without communication. Connected vehicles can share detected features of suspicious individuals, improving tracking and alerting approaching drivers before they park in vulnerable spots. This paper explores this use case, where connected vehicles exchange features over the network upon detecting suspicious individuals. Real-world data analysis shows that connected vehicle surveillance improves detection accuracy by 38%.

Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this direction, but learning DRL control policies that generalize to multiple traffic scenarios is still a challenge. To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning. We employ MRTL for fleet-level emission reduction in mixed traffic using autonomous vehicles as a means of system control. By analyzing the performance of MRTL across nearly 600 signalized intersections and 1200 traffic scenarios, we demonstrate that it emerges as a promising approach to synergize the strengths of DRL and conventional methods in generalizable control.

We present the performance analysis of TCP CUBIC/BBR over V2V Wi-Fi (IEEE 802.11ac). Our measurements focus on three static parking scenarios with different distances at the office area. The results reveal the impact of TCP CUBIC and BBR on data transfer time and TCP metrics. (i) There are two major reasons of fluctuated TCP throughput. The first reason is narrow available bandwidth over V2V Wi-Fi. The second reason is delayed TCP connection establishment due to delayed SYN+ACK and SYN packet retransmission. (ii) The bytes in-flight of TCP CUBIC are dynamically changed by packet retransmission events on V2V Wi-Fi. The loss-based congestion control is not promising the high throughput. We believe that our analysis results provide implications for efficient data transfer over V2V Wi-Fi.

Recently, demand has been growing for development data in research on driver assistance systems. However, important scenes in research vary widely from person to person. For example, developers are interested in collision avoidance might be interested in pedestrian darting out or approaching vehicles. On the other hand, some developers may be interested in traffic scenes. Thus, user preferences vary and making the detection of important scenes are complex. We propose a novel approach to detect important scenes based on user's preferences, novelty of a driving scene and driving data. We annotate important scenes from the NuScenes dataset and confirmed improvement in accuracy from existing important scene detection model.

Fall-on-car incidents (e.g., trees or branches falling on a car) are an underestimated hazard during weather events, mainly affecting vehicles. Unfortunately, drivers are usually unaware of this danger until it occurs. On the other hand, connected vehicles can sense their surroundings, analyze this data with weather forecasts, and alert the driver if there is a risk of a fall-on-car incident. This paper focuses on this use case and demonstrates the Fall-on-Car Prevention (FoP) system. FoP system detects trees and tree branches and alerts drivers when windy conditions are forecasted, allowing early preventative action to be taken while parking. Our evaluation compared to 12 human experts demonstrates that the FoP system can enhance driver awareness of the risk of falling objects.

Vehicular Micro Cloud (VMC) is a group of connected vehicles where vehicles collaborate on a task over the vehicular network. A potential use case of VMC is that micro cloud members transfer data to each other via Vehicle-to-Vehicle (V2V) links, and the data is collaboratively uploaded to remote server (e.g., data center) when the connected vehicles are connected to a Wi-Fi network. In this paper, we focus on this use case and propose collaborative upload by VMC. We demonstrated the feasibility of the proposed method through the large-scale urban simulation (Los Angeles downtown traffic model). Our simulation results showed that the proposed method can reduce the upload data of traditional cellular network-based data upload by 50%.

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.

Federated learning (FL) is widely recognized as a valuable approach for Connected and Automated Vehicles (CAVs) because it facilitates collaborative model development across a multitude of vehicles in a decentralized manner. However, numerous studies on FL algorithms only assessed their performance through experiments conducted in simulated client-server configurations (e.g., where both server and clients run on the same machine) or simplified scenarios that do not account for client downtime. In this paper, we aim to conduct more realistic evaluations for CAV applications leveraging FL. We present a preliminary experimental study as well as offer insights into potential future directions.

Modern cars can detect unsafe driving by comparing the observed behavior of the subject vehicle (i.e., rear vehicles) with normal driving. However, normal driving does not have a standard definition. It changes depending on the situation. In this work, we address this problem and propose edge-assisted unsafe driving detection. In our proposal, instead of learning normal driving, the edge infers the most common unsafe driving patterns. It then shares this knowledge with cars. Cars look for such patterns to detect unsafe driving. Analysis of real-world traffic data shows that edge-assisted unsafe driving detection could detect unsafe behavior of subject vehicles with 90% accuracy.

In this paper, a client-edge-cloud hierarchical federated learning (FL) model has been developed for connected vehicle applications. Generalized models are aggregated on the cloud server, while customized models trained on local data with similar data distribution are aggregated on the edge server, which mitigates the impact of data heterogeneity. To reduce the communication overhead of FL, clients will periodically update to the edge server and edge servers will periodically update to the cloud server. Moreover, we propose a mean field game-based probabilistic device selection scheme. Jointly considering their contributions and the population diversity, a fraction of devices will be selected to join the FL iteration. Taking driving range estimation as an example of connected vehicle applications in the experiment, we have shown that the proposed FL frameworks can increase the prediction accuracy by 28.9% with 5 times fewer clients’ participation, compared with the vanilla FL.

In this article, we demonstrate the first successful application of using reinforcement learning (RL) to develop policies for connected, automated vehicles (CAVs) to mitigate the effects of lane changing in traffic. We discuss how lane changing is a source of wave propagation and disturbance in certain kinds of traffic and propose a RL-based solution for wave damping. While receiving information from the environment and the ego vehicle (connected, non-automated) which is performing a lane change, we train an RL agent, operating as a CAV, to mitigate the waves caused by the lane change. The CAV has an advantage in being able to plan given the information of the vehicle executing the lane change, providing the CAV with anticipatory foresight as well as practical downstream information. At evaluation, the RL-based policy achieves up to a 5.3% improvement in velocity and a 15.9% improvement in throughput. It completely mitigates the formation of waves for certain inflow rates, and facilitates significant improvements for other inflow rates.

Coordinated lane-assignment strategies offer promising solutions for improving traffic conditions. By anticipating and re-positioning connected vehicles in response to potential downstream events, such systems can greatly improve the safety and efficiency of existing networks. Assigning said decisions, however, grows exponentially more complex as the scale of target networks expands. In this paper, we explore solutions to optimal lane assignment at the macroscopic level of traffic, whereby decisions are aggregated across multiple vehicles clustered spatially into sections. This approach reduces some of the challenges around scalability, but introduces dynamical interactions at the microscopic level that render higher-level decision-making complexities. To this point, we provide results demonstrating that reinforcement learning (RL) strategies are capable of generating responses that efficiently coordinate the lateral flow of vehicles across multiple road sections. In particular, we find that RL methods can robustly identify and maneuver vehicles around bottlenecks placed randomly within a given network, and in doing so substantively reduce the the traveling time for both human-driven and connected vehicles.

Unsafe driving has evolved into a public safety crisis. More than half of fatal crashes are due to distracted and aggressive driving. Modern cars have systems to monitor and notify drivers when erratic driving is detected. However, such systems do not help when other nearby vehicles drive unsafely. In this paper, we focus on this use case. We propose a nearby erratic driving detection method in which the ego vehicle observes its surroundings and identifies anomalous driving. We develop and test the proposed method through simulation. Then, we check the feasibility of the proposed method in field trials with multiple test vehicles. Evaluation results show that the proposed method can detect erratic driving on average 4 seconds before the risk of collision becomes maximum with 70% accuracy.

Modern cars can monitor rear vehicles and detect unsafe driving before the risk of rear-end collision becomes maximum. In this paper, we focus on this use case. We propose a Rear Vehicle Behavior Awareness (RVBA) system to prevent rear-end collisions. RVBA detects unsafe driving of rear vehicles and alerts the driver with guidance to reduce the risk of rearend collisions. We tested the RVBA in field experiments using a test vehicle. Experimental results show that RVBA can detect unsafe driving of rear vehicles in 4 seconds on average, with 70% accuracy, before the risk of rear-end collision becomes maximum. Index Terms–erratic movement patterns, rear-end collisions, rear vehicle behavior awareness, unsafe driving detection

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$3-4 Hz$.

Aggressive driving is the leading cause of many fatal crashes. Ego vehicles should detect such dangerous driving behavior on other cars and guide drivers to mitigate collision risk. In this paper, we focus on that use case. We demonstrate a nearby aggressive driving detection system. In nearby aggressive driving detection, the ego vehicle observes the follower vehicle and detects aggressive driving behavior on the follower vehicle. It notifies its driver whenever the follower vehicle exhibits aggressive driving.

Today, modern vehicles can detect other nearby distracted drivers. The next step could be the management of distracted driving, in which a system guides innocent drivers around distracted drivers. The ego vehicle can share detected nearby distracted drivers with the remote server. The remote server tracks them and generates control suggestions (i.e., speed and lane change advisories) to keep innocent drivers away from distracted drivers. In this paper, we focus on that use case. We propose to generate control suggestions (e.g., speed and lane change advisories) for connected vehicles around inattentive drivers. Extensive simulations show that distracted driving management could decrease the collision risk by 78%.

Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic failures during deployment. This paper proposes the Accelerated Policy Evaluation (APE) method, which simultaneously uncovers rare events and estimates the rare event probability in Markov decision processes. The APE method treats the environment nature as an adversarial agent and learns towards, through adaptive importance sampling, the zero-variance sampling distribution for the policy evaluation. Moreover, APE is scalable to large discrete or continuous spaces by incorporating function approximators. We investigate the convergence property of APE in the tabular setting. Our empirical studies show that APE can estimate the rare event probability with a smaller bias while only using orders of magnitude fewer samples than baselines in multi-agent and single-agent environments.

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.

The prevalence of high-speed vehicle-to-everything (V2X) communication will likely significantly influence the future of vehicle autonomy. In several autonomous driving applications, however, the role such systems will play is seldom understood. In this paper, we explore the role of communication signals in enhancing the performance of lane change assistance systems in situations where downstream bottlenecks restrict the mobility of a few lanes. Building off of prior work on modeling lane change incentives, we design a controller that 1) encourages automated vehicles to subvert lanes in which distant downstream delays are likely to occur, while also 2) ignoring greedy local incentives when such delays are needed to maintain a specific route. Numerical results on different traffic conditions and penetration rates suggest that the model successfully subverts a significant portion of delays brought about by downstream bottlenecks, both globally and from the perspective of the controlled vehicles.

Multi-agent trajectory prediction is a challenging task because of the uncertainty of agents’ behaviors, interactions between agents, complex road geometry in urban environments, and imperfect/noisy agent histories. Although accurate prediction results are critical for safe and reliable intelligent driving applications (e.g., decision making, motion planning), some other applications may prefer light-weight and computation-efficient trajectory prediction models to handle dynamically changed environments. In this work, we propose a multi-agent, multi-modal Graph Attention Isomorphism Network (GAIN) based trajectory prediction framework to effectively understand and aggregate long-term interactions across agents. We also take the model complexity and computation efficiency into consideration. Experiments on both pedestrian and vehicle datasets demonstrated the effectiveness of our proposed method.

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.

Aggressive driving became a public safety crisis in the USA. Aggressive drivers tailgate and weave among lanes, which may cause risky events ending up in collisions. Vehicles should be aware of such nearby aggressive driving and notify drivers to keep them away from aggressive ones. In this paper, we focus on that use case. The ego vehicle observes the movements of other nearby cars and detects aggressive driving. We tested the feasibility of the proposed approach through a simulation. Simulation results show that the ego vehicle could identify aggressive driving on other cars with about 94% accuracy.

The detection of abnormal driving behavior is important for safety. However, driving is a combination of both internal (e.g., skills) and external (e.g., road type, traffic conditions) factors that make abnormal driving behavior detection largely subjective. On the other hand, this subjectivity could be handled through the analysis of driving data at multiple levels including the low-level driving action recognition up to high-level inference of a road section. In this paper, we tackle this problem and propose a Hierarchical Abnormal Driving Behavior Detection System (H-ABDS) that is not only mining individual behavior of vehicles but also runs a statistical learning-based technique to understand human driving data at multiple levels. We design a hierarchical architecture to enable abnormal driving behavior detection at the city-scale and demonstrate its benefits through extensive simulations conducted on simulated traffic data. Our preliminary result has shown that H-ABDS can identify all driving anomalies by about 75% accuracy under a certain degree of connected vehicle penetration rates.

The live availability of parking spots is a key enabler of a variety of intelligent parking solutions. A body of research has explored the possibility of using connected vehicles as mobile sensors to measure parking availability. It removes the need for dedicated roadway sensors, achieving wide sensing coverage in a cost-efficient manner. However, the existing crowd-sourcing solutions typically rely on on-board ranging sensors such as radars, lidars and/or sonars to measure locations of the surrounding parked vehicles. In order to lower the barrier for connected vehicles to participate in the crowd-sourced parking availability sensing, this paper investigates a range-free approach to identifying spot-level parking availability. We employ a monocular RGB camera installed in a vehicle and a computer vision-based object tracking mechanism to perceive the surrounding parked vehicles. Analyzing the time series of object tracking results and matching them with a digital map of a parking facility, the system identifies availability of each spot without requiring depth information. The simulation results show that the proposed range-free parking availability sensing system can detect spot-level parking availability with the average accuracy of 83%.

Vehicles at highway merging sections must make lane changes to join the highway. This lane change can generate congestion. To reduce congestion, vehicles should merge so as not to affect traffic flow as much as possible. In our study, we propose a vehicle controller called Deep Merging that uses deep reinforcement learning to improve the merging efficiency of vehicles while considering the impact on traffic flow. The system uses the images of a merging section as input to output the target vehicle speed. Moreover, an embedding network for estimating the controlled vehicle speed is introduced to the deep reinforcement learning network architecture to improve the learning efficiency. In order to show the effectiveness of the proposed method, the merging behavior and traffic conditions in several situations are verified by experiments using a traffic simulator. Through these experiments, it is confirmed that the proposed method enables controlled vehicles to effectively merge without adversely affecting to the traffic flow.

Anomalous drivers with errorable behaviors result in dangerous driving environments on roads, and they significantly increase risk of vehicle collisions for themselves and their surrounding vehicles. Eliminating the impact of anomalous drivers to the surrounding vehicles is very critical to improve driving safety. In this paper, an anomaly management system is developed with the help of connected vehicles to solve the problem. An errorable car-following model is introduced to model the dynamics of anomalous vehicles and to analyze their impacts to other vehicles. The system utilizes connected vehicles to monitor the errorable behaviors of the anomaly drivers and estimates acceleration and lane changing advice for connected vehicles to avoid dangerous behaviors. The anomaly management system is evaluated with both synthetic experiments and microscopic traffic simulations to understand its benefits on mitigating the risk of vehicle collisions. In the synthetic experiments, the proposed system shows its capability of removing collision and near-collision events completely. The microscopic simulation indicates that the system can reduce the probability of collisions by up to 10% and the ratio of time to collision by 22%.

Traffic oscillations on freeways are one of the most important causes of low vehicle energy efficiency, and they result in a large amount of vehicular emissions and high risk of incidents. Most existing studies of freeway eco-driving either focused on one-lane roads or relies on high market penetration rates of connected vehicles, which are not realistic for the early-stage development of connected vehicles. This paper develops an advanced vehicle control system with connected vehicles to mitigate freeway traffic oscillations. This system is able to work for smoothing traffic oscillations on multi-lane freeways with a small number of connected vehicles. It applies the connected vehicles to capture freeway traffic oscillations based on their trajectory information, and a variable speed limit control system is implemented on the connected vehicles to adjust over-passing volumes so as to mitigate downstream traffic oscillations. A mathematical model is proposed to analyze the effect of the system on mitigating traffic oscillations under freeways with multiple lanes. In addition, the system is implemented in both macroscopic and microscopic simulations to understand its benefits on smoothing traffic oscillations along freeway segments and increasing energy efficiency.

The connectivity in vehicular network extends the sensing capability in both the range and quality of the sensing data. One of the most significant benefits is the availability of sensing data from connected vehicles. Leveraging this data in useful ways is an attractive research topic in the community. In this paper, a novel one-pass deep neural network is proposed to implement object detection and the association simultaneously. Considering the bandwidth limitation in the typical vehicular network communication, the proposed algorithm not only highly compresses the feature representation but also maintains the high quality in the detection and association performance. The learning architecture is delicately designed to enhance the task by incorporating multi-modality features. Each modular unit in the system can be appropriately deployed in the vehicle onboard electrical control unit (ECU) and on remote servers to realize a pratical implementation in many applications.

The ability to hand objects to users is a key skill for service robots. While the main purpose of a handover action is to successfully transfer an object to the user, it is also relevant that the robot takes the user's preferences into consideration when deciding which handover strategy to use. In a recent online study, we found evidence that suggests two important facts: (1) Users find value in a robot capable of performing handover tasks in more than one manner, and (2) Different users display different preferences on how they would like to be handed a requested object. Therefore, in this work we are proposing a novel system for learning handover preferences. Our system is evaluated in both simulation and on a real robot using computational perception to estimate human activity and environment type.

Service robots are expected to provide assistance to users by performing useful tasks, such as handing over objects upon request. Most robot-human handover studies implicitly assume that the handover action being executed is the same every time. We postulate that for real scenarios, however, a robot should be capable of accomplishing the handover task using multiple styles of handover, selecting the action that will most likely result in the successful execution of the task and the action that best accommodates different user preferences. This paper addresses the human aspect of this theory, investigating: (1) Will users prefer to have the robot execute more than one type of robot-human handovers? and (2) What factors do users take into account to favor one handover over another? In a survey realized with 62 participants from 2 different countries we conclude that not only is having more than one handover action important for the robot, but also identify 2 factors for selecting the best action autonomously.

Information provision services in vehicle have started according to the progress of data communication infrastructure surrounding vehicles. In such information services, a large amount of data related to vehicles and drivers has been accumulated to the data-center and also been analyzed to provide proper information to drivers. Towards such technical trends surrounding vehicles, a cyber physical system for vehicle application is proposed here. In the proposed system, expected continuous spiral information flow for vehicles and drivers is described. In the data-center, accumulated information has been analyzed by intelligent information processing of data mining, then finally trusted information has been presented to drivers through human machine interface. According to the progress of the information processing technologies in the data-center, several potential applications are introduced mainly based on personal adaptation and big data analysis. It is revealed that destination and route was automatically predicted with 80% accuracy, topics in dialogue were also automatically extracted with 50% precision and driving skill could be separated into two different skill groups. As an experimental challenge to give a better driving advise, a haptic sense device onto a steering wheel was also fabricated and revealed that stimulating Meissner's corpuscles with a frequency range between 30 and 60 Hz was the best for the palm.