Traffic congestion is a pressing issue worldwide, and machine learning (ML) methods are increasingly being used in Intelligent Transportation Systems (ITS) to address this problem. Deep hybrid models, in particular, have emerged as an efficient solution for traffic flow prediction. Among these models, Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) have been widely used to capture the temporal and periodic features of traffic data. The advancements in RNNs provide an opportunity to enhance the performance of existing models. Therefore, this work proposes a BiGRU-BiGRU model with two modules to extract temporal and periodic features from traffic data. Recurrent Neural Networks (RNNs) have proven to perform well using attention mechanism. However, there is a need for attention mechanism that strictly focuses on traffic dynamics and nearby data from the prediction points. Thus, a novel confined attention mechanism is proposed and incorporated into the first module to improve the model’s performance by focusing only on the recent relevant information in the traffic flow sequence. Furthermore, the external features are integrated to improve the model’s prediction performance. The proposed model is evaluated on the publicly available real-world dataset and compared with several baseline state-of-the-art methods. As an outcome, the model offers a reduction in the average value of RMSE, MAE, and MAPE for all the prediction horizons, that is ranged from 5.1% – 20.4%, 7.3% – 27.3%, and 6.1% – 56.6%, respectively.

Energy Management Strategies for Fuel Cell Vehicles: A Comprehensive Review of the Latest Progress in Modeling, Strategies, and Future Prospects

An Overview of Battery Based Electric Vehicle Technologies With Emphasis on Energy Sources, Their Configuration Topologies and Management Strategies

Urban rail transit systems (URTSs) have increasingly become the backbone of modern public transportation, attributed to their unparalleled convenience, high efficiency, and commitment to sustainable green energy. The authors witness a global resurgence of urban rail transit, it becomes evident that most existing URTS still operate on a level of suboptimal intelligence, with their operation and maintenance methods lagging behind other advanced urban transit systems. URTS generates considerable data, offering substantial opportunities for service quality enhancements. machine learning (ML), with its demonstrated proficiency in extracting valuable insights from vast data, holds significant promise in the quest to empower URTS. This survey presents a comprehensive exploration of the potential application of ML in URTS. Initially, they delve into the existing challenges of URTS, thereby elucidating the compelling motivation behind the integration of ML into these systems. They then propose a taxonomy of ML paradigms and techniques, discussing indepth their potential applications in URTS, encompassing perception, prediction, and optimization tasks. Subsequently, they scrutinize a plethora of ML-empowered URTS application scenarios, including but not limited to obstacle perception, infrastructure perception, communication and cybersecurity perception, passenger flow prediction, train delay prediction, fault prediction, remaining useful life (RUL) prediction, train operation and control optimization, train dispatch optimization, and train ground communication optimization. Finally, they present an insightful discussion on the challenges and future directions for URTS, aiming to harness the full potential of ML techniques to deliver superior service and performance.

Autonomous vehicles offer many benefits to driving, including safety, comfort, and efficiency. The significant extended benefit of autonomy in traffic comes from the connectivity through communications among vehicles, vehicle and infrastructure, and vehicle and other road users (also known as V2V, V2I, and V2X.) Connected Autonomous Vehicles or CAVs connectivity allows automated trajectories, plans, and coordinated speed adaptations that are safer, enhance traffic throughput and reduce energy consumption. CAVs will have more comprehensive situational awareness in the vehicle’s vicinity for safety assurance. Driving tasks like lane changing, merging, navigating automated intersections, and various collision avoidance scenarios benefit significantly from the connectivity and the enhanced collective perception of the surrounding areas. Appropriate algorithms based on CAVs’ connectivity data can also improve driving task decision-making and navigation plans within the traffic. This talk presents a few examples of advanced methods and algorithms that demonstrate various benefits and challenges of CAVS through simulation and laboratory experiments. It serves as an overview referring to reference co-authored articles by the presenter.

The IEEE Workshop on Connected and Autonomous Vehicles (CAV) was organized to bring experts from intelligent transportation systems (ITS) in this specific area together to discuss some of the latest findings and research results, applicable technologies, issues and limitations, standards development activities, societal impacts, and future research and development directions. This workshop was sponsored by the IEEE Intelligent Transportation Systems (ITS) Society, with the support of the Mechanical Engineering Department of Virginia Tech. Dr. Azim Eskandarian, Nicholas, and Rebecca Des Champs Chair and the Head of the Mechanical Engineering Department at Virginia Tech at the time of this workshop (and currently the Dean of Engineering and the Alice T. and William H. Goodwin Jr. Endowed Chair at Virginia Commonwealth University) chaired this workshop. Dr. Monastir Abbas, Professor of Civil Engineering at Virginia Tech, was the Workshop’s Co-Chair. They invited 19 renowned experts, each specializing in specific areas of CAV. They were members of academia, industry, government, and related professional organizations, including IEEE, ITE, and SAE. The workshop was held on October 27–28, 2022, at Hyatt Hotel, 7901 Tysons One Place, Tysons Corner, VA 22102 USA.

This paper introduces a novel approach for addressing a fundamental challenge in pedestrian trajectory prediction: reliable future state estimation in the presence of sensor noise and uncertain perception. It is common for traditional prediction models to produce overconfident and error-prone deterministic estimates. Previous research exploring probabilistic estimation methods often overlooked inherent noise in upstream perception data which is a crucial factor under adverse conditions like bad weather or occlusion. While Bayes filters are adept at integrating data from noisy sensors, they falter in handling non-linearities and long-term forecasting. Our proposed approach is an end-to-end estimator capable of handling noisy sensor data to deliver robust predictions of future states with uncertainty bounds while factoring in upstream perception uncertainty. This is achieved through an innovative encoder-decoder-based deep ensemble network designed to capture both perception and prediction uncertainty during trajectory prediction. We benchmark our model against other established approximate Bayesian inference methods on publicly available pedestrian datasets. The results demonstrate that our deep ensembles not only yield more robust predictions but also capable of reliable predictions on out-of-distribution pedestrian trajectories. Our method plays a key role in improving dynamic agent trajectory prediction, offering a more precise and robust framework for future state estimation.

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This article reviews graph-based hazardous event detection methods for automated and autonomous vehicles. Traditional methods have often fallen short due to the complexity of events that involve a multitude of variables with intricate relationships. Therefore, this research explores the opportunity presented by graph-based methods for relational reasoning. Reasoning uses graph structures that can organize heterogeneous data about the scene and its relationships. The article reviews and categorizes state-of-the-art methods, datasets, and evaluation metrics to provide a comprehensive overview of the latest advancements in the field, as well as key research opportunities and open challenges.

Vehicle platooning has become a topic of substantial interest for the development of safer and more efficient means of transportation. It is described as a string of connected autonomous vehicles traveling closely, maintaining certain inter-vehicular distances at a set speed boosting the road capacity, improving safety, and lowering adverse environmental significance. Several platooning concepts are revealed. A unified integrated platoon control architecture is constructed, employing the aforementioned concepts. Several control algorithms are investigated, optimized, and compared to manage the longitudinal inter-vehicular spacing distances of a formed platoon, as well as the lateral platoon motion tracking. The outperformed controllers are utilized in an integrated platooning scenario. Simulations are visualized using the multi-robot systems intelligent transportation systems (MRS-ITSs) visualization tool for further realization of the results.

Survey of Air, Sea, and Road Vehicles Research for Motion Control Security

Vehicular Communication Network Enabled CAV Data Offloading: A Review

Edge Intelligence in Intelligent Transportation Systems: A SurveyT. Gong, L. Zhu, F. R. Yu, and T. TangEdge intelligence (EI) is becoming one of the research hotspots among researchers, which is believed to help empower intelligent transportation systems (ITS). ITS generates a large amount of data at the network edge by millions of devices and sensors. Data-driven artificial intelligence (AI) is at the core of ITS development. By pushing the AI frontier to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network, and better use of edge big data. This article surveys edge intelligence in intelligent transportation systems. The authors first introduce the challenges ITS faces and explain the motivation for using EI in ITS. They then explore the framework of using EI in ITS, including the EI-based ITS architecture, the data gathering and communication methods, the data processing and service delivery, and the performance indexes. The enabling technologies, such as AI models, the Internet of Things, and edge computing technologies used in EI-based ITS, are reviewed intensively. They discuss edge intelligence applications and research fields in ITS in depth. Typical application scenarios, such as autonomous driving, vehicular edge computing, intelligent vehicular transportation system, unmanned aerial vehicle (UAV) in ITS environment, and rail transportation control and management, are explored. The general platforms of EI, the EI training and inference in ITS, as well as the benchmark datasets, are introduced. Finally, they discuss some of the challenges and future directions of using EI in ITS.

Resilient Countermeasures Against Cyber-Attacks on Self-Driving Car Architecture

Position-Based Emergency Message Dissemination Schemes in the Internet of Vehicles: A Review

Transportation 5.0: The DAO to Safe, Secure, and Sustainable Intelligent Transportation Systems

In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee on Transportation 5.0 with the mission of promoting and transforming the deployment of advanced and innovative technologies, especially Artificial Intelligence in transportation. This paper briefly summarizes our main research and findings over the last decade. Transportation Foundation Models, Transportation Scenarios Engineering, and Transportation Operating Systems have been identified as the main directions for the research and development of next-generation intelligent transportation systems.

Reliable object detection using cameras plays a crucial role in enabling autonomous vehicles to perceive their surroundings. However, existing camera-based object detection approaches for autonomous driving lack the ability to provide comprehensive feedback on detection performance for individual frames. To address this limitation, we propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms and provides frame-by-frame feedback on detection quality. The DQI is generated by combining the intensity of the fine-grained saliency map with the output results of the object detection algorithm. Additionally, we have developed a superpixel-based attention network (SPA-NET) that utilizes raw image pixels and superpixels as input to predict the proposed DQI evaluation metric. To validate our approach, we conducted experiments on three open-source datasets. The results demonstrate that the proposed evaluation metric accurately assesses the detection quality of camera-based systems in autonomous driving environments. Furthermore, the proposed SPA-NET outperforms other popular image-based quality regression models. This highlights the effectiveness of the DQI in evaluating a camera’s ability to perceive visual scenes. Overall, our work introduces a valuable self-evaluation tool for camera-based object detection in autonomous vehicles.

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Investigating the Prospect of Leveraging Blockchain and Machine Learning to Secure Vehicular Networks: A Survey

Displaying the Driving State of Automated Vehicles to Other Road Users: An International, Virtual Reality-Based Study as a First Step for the Harmonized Regulations of Novel Signaling Devices

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

Deep Reinforcement Learning for Autonomous Driving: A SurveyR. Kiran, I. Sobh, V. Talpaert, P. Mannion, A. A. Al Sallab, S. Yogamani, and P. PérezWith the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high-dimensional environments. This review summarizes deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in the real-world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, and inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test, and robustify existing solutions in RL are discussed.

Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges

A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

Human–Machine Interaction in Intelligent and Connected Vehicles: A Review of Status Quo, Issues, and Opportunities

This survey paper reviews the risk assessment methodology for autonomous vehicles. The paper explains the necessity of real-time risk assessment in conjunction with traditional approaches, especially for highly automated driving. The current methodologies are segmented into different approaches to help identify risk in the aspects of quantitative or qualitative measurements. Further analyses are conducted for each methodology used during development or real-time usage. The outcome of this paper includes a recommended list that addresses each of these methodologies for their suitability toward ISO 26262 and ISO/PAS 21448. In addition, this paper addresses the importance of determinism and uncertainty in different risk assessment methodologies, especially in the usage of AI and machine learning.

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and DatasetsI. Kotseruba and J. K. TsotsosSince first cars appeared on the streets, driver inattention has been a safety concern. Recently, advanced driver assistance systems (ADAS) and autonomous driving have been introduced to tackle this issue. In this article, the authors review a broad range of vision-based algorithms proposed in the past decade that model drivers’ attention for assistive and self-driving systems and detect drivers’ gaze for monitoring applications. Furthermore, they provide a brief theoretical background on visual attention in the driving domain, survey public datasets with attention-related annotations, and discuss remaining open problems in this research area.

Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and OutlooksK. Muhammad, T. Hussain, H. Ullah, J. D. Ser, M. Rezaei, N. Kumar, M. Hijji, P. Bellavista, and V. H. C. de AlbuquerqueScene understanding in autonomous driving utilizes sensory data for contextual information extraction and decision making. Related research directions include person/vehicle detection/segmentation, their transition analysis, lane change, and turns detection, among others. Unfortunately, these tasks seem insufficient to completely develop fully autonomous vehicles, i.e., achieving level-5 autonomy, traveling just like human-controlled cars. This statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. Specifically, the authors define, analyze, and examine current achievements in scene understanding, covering the generic scene understanding pipeline, investigating state-of-the-art performance levels, informing about the time complexity analysis of avant-garde models, and highlighting major triumphs and limitations identified by recent research efforts. The authors also include a thorough discussion on available datasets and challenges that, even if lately confronted by researchers, remain open to date. Finally, research directions are offered to welcome researchers/practitioners to this exciting domain.

The recent progress in autonomous vehicle research and development has led to increasingly widespread testing of fully autonomous vehicles on public roads, where complex traffic scenarios arise. Along with these vehicles, partially autonomous vehicles, manually-driven vehicles, pedestrians, cyclists, and some animals can be present on the road, to which autonomous vehicles must react. This study focuses on a comprehensive survey of the literature on motion prediction and state estimation of vehicles and VRUs, which are essential for path planning and navigation functionalities of an autonomous vehicle. Motion prediction and state estimation methods utilize the vehicle’s own sensory perception capabilities and information obtained through cooperative perception from V2V and V2X connections. This survey summarizes the significant progress that has been made in both categories, discusses the most promising results to date and outlines critical research challenges that need to be overcome to achieve full autonomy, from an ego vehicle’s perspective in mixed traffic environments.

This article proposes a model to estimate the probability of a vehicle reaching a near-term goal state using one or multiple lane changes based on parameters corresponding to traffic conditions and driving behavior. The proposed model not only has broad application in path planning and autonomous vehicle navigation, it can also be incorporated in advance warning systems to reduce traffic delay during recurrent and non-recurrent congestion. The model is first formulated for a two-lane road segment through systemic reduction of the number of parameters and transforming the problem into an abstract statistical form, for which the probability can be calculated numerically. It is then extended to cases with a higher number of lanes using the law of total probability. VISSIM simulations are used to validate the predictions of the model and study the effect of different parameters on the probability. For most cases, simulation results are within 4% of model predictions, and the effect of different parameters such as driving behavior and traffic density on the probability match our expectation. The model can be implemented with near real-time performance, with computation time increasing linearly with the number of lanes.

This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic congestion, capacity drop, and delay. When an incident blocking the lanes ahead is detected, Sentinel calculates the probability of leaving the blocked lane(s) before reaching the incident point at each time step. It advises the vehicle to leave the blocked lane(s) when that probability drops below a certain threshold, as the vehicle nears the congestion boundary. By doing this, Sentinel reduces the number of late-stage lane changes of vehicles in the blocked lane(s) trying to move to other lanes, and distributes those maneuvers upstream of the incident point. A simulation case study is conducted in which one lane of a four-lane section of the I-66 interstate highway in the U.S. is temporarily blocked due to an incident, to understand how Sentinel impacts traffic flow and how different parameters - traffic flow, system penetration rate, and incident duration - affect Sentinel’s performance. The results show that Sentinel has a positive impact on traffic flow, reducing average delay by up to 37%, particularly when it has a considerable penetration rate. Working alongside Traffic Incident Management Systems (TIMS), Sentinel can be a valuable asset for reducing traffic delay and potentially saving billions of dollars annually in costs associated with congestion caused by freeway incidents.

A Survey on Electric Buses—Energy Storage, Power Management, and Charging SchedulingR. Deng, Y. Liu, W. Chen, and H. LiangIn recent years, aiming to reduce the metropolitan air pollution caused by fossil fuel-powered vehicles, the electrification of transportation, such as electric vehicles (EVs) and electric buses (EBs), has attracted great attention from the automobile industry, academia, and public transportation. EBs, driven by decarbonized electricity, can reduce the air pollution and noise level. Besides, they can also recover electricity from regenerative braking. This survey first introduces the important components of EBs, including energy storage systems, powertrains, interleaving elements and electric motors, and driving cycles. Then, it reviews the existing research topics of EBs, including the energy storage system sizing, power/energy management, range remedy methods, charging design/scheduling, and trial projects. Finally, extending from existing studies, it further proposes the future research opportunities and ongoing challenges, such as extending EV related research to EBs, EB charging demand modeling, and EB impact on power systems.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

A Review of Big Data Applications in Urban Transit Systems

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

What Happens for a ToF LiDAR in Fog?

Two-Stage Scalable Air Traffic Flow Management Model Under UncertaintyG. G. N. Sandamali, R. Su, K. L. K. Sudheera, Y. Zhang, and Y. ZhangIn order to efficiently balance the current and future air traffic demands with the system capacity, a proper air traffic flow management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the air transportation system (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, the authors propose an ATFM framework for scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, the authors propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an integer linear programming (ILP) problem, and a mixed-integer linear programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a maximum weighted independent set (MWIS) finding problem.

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

Presents summaries of the papers included in this issue of the publication.

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Driver Inattention Detection in the Context of Next-Generation Autonomous Vehicles Design: A Survey

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

In 2015, the Board of Governors of IEEE Intelligent Transportation Systems Society had approved the proposal to name the Best Paper Award in IEEE Transactions on Intelligent Transportation Systems as the George N. Saridis Best Transactions Paper Award. After nearly five years of preparation and planning, and almost one year of hard and concentrated effort by the Award Committee, we are pleased to announce the 2014–2017 George N. Saridis Best Transactions Paper Award for papers published in the IEEE Transactions on Intelligent Transportation Systems.

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Adaptive Cruise Control (ACC) systems utilize distance sensors and control algorithms to enable a vehicle to follow its preceding vehicle with desired headways. There are situations when distance sensors may lose the target, i.e., the preceding vehicle, when unfavorable weather conditions result in low reflectance or the preceding vehicle escapes the sensor’s angle of view on curvy roads. In these situations, a common strategy is to suppress acceleration and maintain current speed (Cruise Control) until the sensor detects the preceding vehicle again. In this paper, we propose a new solution which integrates odometry and inter-vehicular communication to enable vehicle following for the period of target detection loss. With intervehicular communication, a following vehicle can obtain the odometry data of its preceding vehicle to compute the intervehicle distance. This work focuses on the design of an ACC system that can enable vehicle following during short-term target detection loss. The feasibility of the system design is validated through mobile robot experiments. Autonomous lane keeping capability is developed such that the mobile robots move according to the center of the lane. The robot following experiment results show that with accurate odometry and lane centering, the proposed system design can maintain desired headway following.

Abstract Vehicularad hocnetworks (VANETs) can support a wide range of future cooperative safety and efficiency applications. However, the node density and high demand for wireless media in these networks can lead to theTimeslot Boundary Synchronization Problem, in which increased transmission collisions occur due to back‐off timer synchronization. This paper proposes an enhancement to the wireless access in vehicular environments (WAVE) communications architecture to address this problem, called RAndom Propagation Initiation Delay for the Distributed Coordination Function (RAPID DCF). The effectiveness of RAPID DCF is evaluated through simulations of both single‐hop and multi‐hop emergency messages. In these simulations, RAPID DCF was able to improve message delivery rates by as much as 35% and reduce multi‐hop message latency by as much as 18%. Copyright © 2010 John Wiley & Sons, Ltd.

Steering control systems have been used to develop vehicle automated lane change maneuvers or evasive maneuvers for collision avoidance. Most of these systems have used predetermined desired trajectories to perform the required maneuvers. In this study, an optimal trajectory is found while ensuring minimization of lateral acceleration throughout the maneuver. Collocation technique was used to numerically solve the nonlinear programming problem. The results show a near optimal trajectory can be achieved. The generated trajectory is compared to that of a fifth-order polynomial. The resultant trajectory was substantially better than the polynomial one, with both a lower peak and the overall lateral accelerations.

The Timeslot Boundary Synchronization Problem occurs when timeslot boundaries become aligned leading to an increase in the probability of message collisions. This alignment, which can occur in Vehicular Ad Hoc Networks using the 802.11p protocols, adversely affects the performance of multi-hop message broadcasts. This paper describes the causes of this phenomenon and describes link and network layer design guidelines to address the problem. An integrated simulation system, which combines widely used wireless network and microscopic vehicle simulators, was used to simulate multi-hop emergency message broadcast in dense traffic situations. The simulation results show that addressing the Timeslot Boundary Synchronization Problem can reduce the total latency of multi-hop messages by 20.3% to 26.6%.

Vehicle heartbeat messages will enable a wide range of safety and efficiency applications. These messages, containing the position, kinematics, and state of a vehicle, will be sent over a wireless network to nearby vehicles at a high frequency. These messages will include a digital signature and a public key certificate to allow message recipients to validate the contents of a message. To minimize on the overhead, standards governing this network allow for the transmission of short certificate IDs instead of the full certificate. This paper proposes two distributed Certificate ID Scheduling algorithms, one in which a certificate is sent every nthmessage and another in which vehicles send full certificates only when a new neighbor is detected. Privacy considerations for certificate ID scheduling are analyzed, and the effectiveness of the protocols is evaluated through simulation of these heart-beat messages in a highway environment. These simulations show the first algorithm is most appropriate for high density, two-way traffic while the second algorithm can produce bandwidth savings that are close to the maximum for one-way or low density traffic.

Current link-layer protocols for safety-related intervehicle communication (IVC) networks suffer from significant scalability and security challenges. Carrier sense multiple-access approaches produce excessive transmission collisions at high vehicle densities and are vulnerable to a variety of denial of service (DoS) attacks. Explicit time slot allocation approaches tend to be limited by the need for a fixed infrastructure, a high number of control messages, or poor bandwidth utilization, particularly in low-density traffic. This paper presents a novel adaptation of the explicit time slot allocation protocols for IVC networks. The protocol adaptive space-division multiplexing (ASDM) requires no control messages, provides protection against a range of DoS attacks, significantly improves bandwidth utilization, and automatically adjusts the time slot allocation in response to changes in vehicle densities. This paper demonstrates the need for and the effectiveness of this new protocol. The exposures of the current proposals to attacks on availability and integrity, as well as the improvements effected by ASDM, are analytically evaluated. Furthermore, through simulation studies, ASDM's ability to provide message delivery guarantees is contrasted with the inability of the current IVC proposals to do the same

Intervehicle communication (IVC) networks, a subclass of mobile ad hoc networks (MANETs), have no fixed infrastructure and instead rely on the nodes themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, IVC networks exhibit characteristics that are dramatically different from many generic MANETs. This paper elicits these differences through simulations and mathematical models and then explores the impact of the differences on the IVC communication architecture, including important security implications.

This paper looks into recent developments and research trends in collision avoidance/warning systems and automation of vehicle longitudinal/lateral control tasks. It is an attempt to provide a bigger picture of the very diverse, detailed and highly multidisciplinary research in this area. Based on diversely selected research, this paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation. Human factor studies and legal issues are analyzed as well as control algorithms. Drivers' comfort and well being, increased safety, and increased highway capacity are among the most important initiatives counted for automation. However, sometimes these are contradictory requirements. Relying on an analytical survey of the published research, we will try to provide a more clear understanding of the impact of automation/warning systems on each of the above-mentioned factors. The discussion of sensory issues requires a dedicated paper due to its broad range and is not addressed in this paper.