Velocity control in car-following behavior with autonomous vehicles using reinforcement learning
Car-following behavior is a common driving behavior. It is necessary to consider the
following vehicle in the car-following model of autonomous vehicle (AV) under the …
following vehicle in the car-following model of autonomous vehicle (AV) under the …
Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections
Intersections are essential road infrastructures for traffic in modern metropolises. However,
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
Cooperative multi-agent reinforcement learning for large scale variable speed limit control
Y Zhang, M Quinones-Grueiro… - … on Smart Computing …, 2023 - ieeexplore.ieee.org
Variable speed limit (VSL) control has emerged as a promising traffic management strategy
for enhancing safety and mobility. In this study, we introduce a multi-agent reinforcement …
for enhancing safety and mobility. In this study, we introduce a multi-agent reinforcement …
Trafficgen: Learning to generate diverse and realistic traffic scenarios
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous
driving systems in simulation. This work introduces a data-driven method called TrafficGen …
driving systems in simulation. This work introduces a data-driven method called TrafficGen …
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning
X Lu, FX Fan, T Wang - arXiv preprint arXiv:2306.15968, 2023 - arxiv.org
Reinforcement Learning (RL) has made promising progress in planning and decision-
making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL …
making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL …
Network-scale traffic signal control via multiagent reinforcement learning with deep spatiotemporal attentive network
The continuous development of intelligent traffic control systems has a profound influence
on urban traffic planning and traffic management. Indeed, as big data and artificial …
on urban traffic planning and traffic management. Indeed, as big data and artificial …
Flow: A modular learning framework for mixed autonomy traffic
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation
systems through improved safety, efficiency, and access to mobility. However, the …
systems through improved safety, efficiency, and access to mobility. However, the …
Adaptive road configurations for improved autonomous vehicle-pedestrian interactions using reinforcement learning
The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique
opportunities for the design and management of future urban road infrastructure. In light of …
opportunities for the design and management of future urban road infrastructure. In light of …
Integrated decision and control: Towards interpretable and computationally efficient driving intelligence
Decision and control are core functionalities of high-level automated vehicles. Current
mainstream methods, such as functionality decomposition and end-to-end reinforcement …
mainstream methods, such as functionality decomposition and end-to-end reinforcement …
Bio-inspired collision avoidance in swarm systems via deep reinforcement learning
Autonomous vehicles have been highlighted as a major growth area for future transportation
systems and the deployment of large numbers of these vehicles is expected when safety …
systems and the deployment of large numbers of these vehicles is expected when safety …