Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
A systematic survey of control techniques and applications in connected and automated vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Adaptive on-ramp merging strategy under imperfect communication performance
X Tong, Y Shi, Q Zhang, S Chen - Vehicular Communications, 2023 - Elsevier
On-ramp merging is one of the important V2X (Vehicle-to-Everything) applications and is
critical for both driving safety and traffic efficiency. The ramp vehicle needs to get information …
critical for both driving safety and traffic efficiency. The ramp vehicle needs to get information …
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
[HTML][HTML] Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
Abstract Model-based reinforcement learning (RL) is anticipated to exhibit higher sample
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
Revisiting parameter sharing in multi-agent deep reinforcement learning
Parameter sharing, where each agent independently learns a policy with fully shared
parameters between all policies, is a popular baseline method for multi-agent deep …
parameters between all policies, is a popular baseline method for multi-agent deep …
Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …
ensuring trustworthiness remains a formidable challenge when applying this technology to …
Communication-efficient decentralized multi-agent reinforcement learning for cooperative adaptive cruise control
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with
enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs …
enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs …