Intersection control with connected and automated vehicles: A review
Purpose-This paper aims to review the studies on intersection control with connected and
automated vehicles (CAVs). Design/methodology/approach-The most seminal and recent …
automated vehicles (CAVs). Design/methodology/approach-The most seminal and recent …
Model-free deep reinforcement learning for urban autonomous driving
Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …
and multi-agent interactions. Current decision making methods are mostly manually …
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …
perception, decision and control problems in an integrated way, which can be more …
Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections
Taking advantage of both vehicle-to-everything (V2X) communication and automated driving
technology, connected and automated vehicles are quickly becoming one of the …
technology, connected and automated vehicles are quickly becoming one of the …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Artificial intelligence for vehicle-to-everything: A survey
W Tong, A Hussain, WX Bo, S Maharjan - IEEE Access, 2019 - ieeexplore.ieee.org
Recently, the advancement in communications, intelligent transportation systems, and
computational systems has opened up new opportunities for intelligent traffic safety, comfort …
computational systems has opened up new opportunities for intelligent traffic safety, comfort …
Automated eco-driving in urban scenarios using deep reinforcement learning
Urban settings are challenging environments to implement eco-driving strategies for
automated vehicles. It is often assumed that sufficient information on the preceding vehicle …
automated vehicles. It is often assumed that sufficient information on the preceding vehicle …
Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range …
The connectivity aspect of connected autonomous vehicles (CAV) is beneficial because it
facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External …
facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External …
Crossfuser: Multi-modal feature fusion for end-to-end autonomous driving under unseen weather conditions
Multi-modal fusion is a promising approach to boost the autonomous driving performance
and has already received a large amount of attention. Meanwhile, to increase driving …
and has already received a large amount of attention. Meanwhile, to increase driving …
Safe-state enhancement method for autonomous driving via direct hierarchical reinforcement learning
Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …
making problem, where safety in the form of state constraints is of great significance in the …