Search Results for « Connected Automated Vehicles (CAVs) »

This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle-level eco-driving behaviors and intersection-level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi-Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle-based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning-based Trajectory Planning Algorithm (MLTPA) to guide CAVs in executing Eco-Approach and Departure (EAD) maneuvers. The framework is evaluated across varying CAV proportions and powertrain types to assess its effects on mobility and energy performance. Experimental results conducted in a 4*4 real-world network demonstrate that the MARL-based TSC method outperforms the baseline model (i.e., Webster method) in speed, fuel consumption, and idling time. In addition, with MLTPA, HONEST-CAV benefits the traffic system further in energy consumption and idling time. With a 60% CAV proportion, vehicle average speed, fuel consumption, and idling time can be improved/saved by 7.67%, 10.23%, and 45.83% compared with the baseline. Furthermore, discussions on CAV proportions and powertrain types are conducted to quantify the performance of the proposed method with the impact of automation and electrification.

In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs.

Alternative intersection designs (AIDs) can improve the performance of an intersection by not only reducing the number of signal phases but also change the configuration of the conflicting points by re-routing traffic. However the AID studies have rarely been extended to Connected and Automated Vehicle (CAV) which is expected to revolutionize our transportation system. In this study, we investigate the potential benefits of CAV to two AIDs: the diverging diamond interchange (DDI) and the restricted crossing U-turn intersection. The potential enhancements of AID, CAV, and the combination of both are quantified via microscopic traffic simulation. We found that CAV is able to positively contribute to the performance of an intersection. However, converting an existing conventional diamond interchange (CDI) to a diverging one is a more effective way according to the simulation results. DDI improves the throughput of a CDI by 950 vehicles per hour, a near 20% improvement; whereas with full penetration of CAV, the throughput of a CDI is increased only by 300 vehicles per hour. A similar trend is observed in the average delay per vehicle as well. Furthermore, we assess the impact for the driver's confusion, a concern for deploying AIDs, on the traffic flow. According to the ANOVA test, the negative impacts of driver's confusion are of statistical significance.

The connected and autonomous vehicle (CAV) is the next-generation mobility service—powered by intelligent automation and robust communication—which is aimed at replacing human-maneuvered vehicles with the software agent matching or even exceeding the human-level intelligence, control, and agility and minimizing errors. The next generation of transportation and mobility envisions safe, reliable, agile, automated, trustworthy, and service-based mobility architecture. The architecture should be able to eliminate human errors by using intelligent decision-making software agents based on the situational and behavioral information collected by sensors and transceivers through communication. Apart from that, service-based architecture removes the concept of vehicle ownership and incorporates more diversity in passengers, including the disabled and elderly people. CAV is the evolving technology to achieve the envisioned goals of future mobility and transportation. This chapter sheds light on cyber-physical vulnerabilities and risks that originated in informational technology (IT), operational technology (OT), and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence.

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios.

Connected automated vehicles (CAVs) are typically equipped with communication devices (e.g., dedicated short range communications (DSRC)) and on-board sensors (e.g., Radar, Lidar, Camera, etc.). Communication devices would enable the exchange of real-time information between vehicles and infrastructures via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) channels. Sensors equipped in vehicles are providing various vehicle sensor data (VSD) such as the CAV’s GPS location, speed and moving direction (trajectory). Existing studies have shown the effectiveness of using CAV trajectories as input in many traffic control models. However, it can be expected that CAVs and human-driven vehicles (HVs) will co-exist on the transportation network in a long period. Hence, to support various traffic control tasks, it is critical to develop a reliable model to understand the real-time traffic pattern in a mixed CAV and HV environment. To satisfy such needs, this project firstly introduces a novel macroscopic traffic flow model which treats CAVs and HVs as separate groups, where a new set of factors are introduced to represent the speed change of HVs due to following CAVs in the traffic stream. Then grounded on the traffic flow model, an optimization function will make real-time adjustment of CAVs’ desired speeds for minimizing the total freeway travel delays. Selecting a segment of I-15 in Salt Lake City as the study site, the research team conducts extensive simulation experiments to evaluate highway performance under different demand levels and CAV penetration rates. Corresponding data analysis reveals that there should exist a critical CAV ratio that can greatly reduce the speed difference between CAVs and HVs in the traffic stream, given the demand pattern. Further discussions have highlighted the need of setting up guidance on highway capacity analysis under the CAV environment.

This work gives introduction to traffic control by connected automated vehicles. The influence of vehicle control on vehicular traffic and traffic control strategies are discussed and compared. It is highlighted that vehicle-to-everything connectivity allows connected automated vehicles to access the state of the traffic behind them such that feedback can be utilized to mitigate evolving congestions. Numerical simulations demonstrate that such connectivity-based traffic control is beneficial for smoothness and energy efficiency of highway traffic. The dynamics and stability of traffic flow, under the proposed controllers, are analyzed in detail to construct stability charts that guide the selection of stabilizing control gains.

Intelligent transportation systems (ITS) leverage information and communication technologies to enhance the efficiency, safety, and sustainability of road transportation.Connected vehicles play a crucial role in ITS as they enable communication with other vehicles and infrastructure, facilitating more efficient traffic management and safer driving experiences.They can provide real-time traffic information to help drivers avoid congestion and accidents, as well as navigation and route planning recommendations.Additionally, it can communicate with traffic signal equipment to optimise signal timing, reducing wait times and queue lengths.Implementation of connected vehicles in intelligent transportation systems will revolutionise the way we drive.However, many challenges need to be addressed to achieve maximum potential, including privacy and security issues, data processing and storage, the development of standards and regulations across all platforms, and the establishment of new communication protocols and system architectures.This Special Issue on Enhancing Safety in Connected Automated Vehicles (CAVs) for Mixed Traffic Environments of the IET INTELLIGENT TRANSPORT SYSTEMS presents recent progress in theories, methods, and applications addressing safety-critical challenges in autonomous and intelligent transportation systems.After a rigorous peer-review process, 12 papers were selected from a wide range of international submissions.The accepted papers are grouped into seven principal research themes, each reflecting a key aspect of the Special Issue's scope.

In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neural network, following which, the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on-the-go based on the predicted steering angle and other parameters such as actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity.

The automation and connectivity of CAV inherit most of the cyber-physical vulnerabilities of incumbent technologies such as evolving network architectures, wireless communications, and AI-based automation. This book chapter entails the cyber-physical vulnerabilities and risks that originated in IT, OT, and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence. To deal with the security threats in high-speed, high dimensional, multimodal data and assets from eccentric stakeholders of the CAV ecosystem, this chapter presents and analyzes some of the state of art deep learning-based threat intelligence for attack detection. The frontiers in deep learning, namely Meta-Learning and Federated Learning, along with their challenges have been included in the chapter. We have proposed, trained, and tested the deep CNN-LSTM architecture for CAV threat intelligence; assessed and compared the performance of the proposed model against other deep learning algorithms such as DNN, CNN, LSTM. Our results indicate the superiority of the proposed model although DNN and 1d-CNN also achieved more than 99% of accuracy, precision, recall, f1-score, and AUC on the CAV-KDD dataset. The good performance of deep CNN-LSTM comes with the increased model complexity and cumbersome hyperparameters tuning. Still, there are open challenges on deep learning adoption in the CAV cybersecurity paradigm due to lack of properly developed protocols and policies, poorly defined privileges between stakeholders, costlier training, adversarial threats to the model, and poor generalizability of the model under out of data distributions.