Safe reinforcement learning for urban driving using invariably safe braking sets
H Krasowski, Y Zhang, M Althoff - 2022 IEEE 25th International …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …
Game theory-based decision-making and iterative predictive lateral control for cooperative obstacle avoidance of guided vehicle platoon
This paper presents a systematic decision-making and lateral control framework to realize
cooperative obstacle avoidance (OA) of the guided vehicle platoons safely and integrally in …
cooperative obstacle avoidance (OA) of the guided vehicle platoons safely and integrally in …
Improve generalization of driving policy at signalized intersections with adversarial learning
Intersections are quite challenging among various driving scenes wherein the interaction of
signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving …
signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving …
Curse of rarity for autonomous vehicles
The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—
presents significant challenges in ensuring the safety of autonomous vehicles using deep …
presents significant challenges in ensuring the safety of autonomous vehicles using deep …
Learning to control autonomous fleets from observation via offline reinforcement learning
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …
Neural MPC-Based Decision-Making Framework for Autonomous Driving in Multi-Lane Roundabout
The multi-lane roundabout poses significant challenges for autonomous driving due to its
complex road structure and traffic conditions. To address these challenges, this paper …
complex road structure and traffic conditions. To address these challenges, this paper …
Proactive Motion Planning for Uncontrolled Blind Intersections to Improve the Safety and Traffic Efficiency of Autonomous Vehicles
S Park, Y Jeong - Applied Sciences, 2022 - mdpi.com
For the last two decades, autonomous vehicles have been proposed and developed to
extend the operational design domain from the motorway to urban environments. However …
extend the operational design domain from the motorway to urban environments. However …
基于强化学习的自动驾驶决策研究综述
金立生, 韩广德, 谢宪毅, 郭柏苍, 刘国峰, 朱文涛 - 汽车工程, 2023 - qichegongcheng.com
强化学习的发展推动了自动驾驶决策技术的进步, 智能决策技术已成为自动驾驶领域高度关注的
要点问题. 本文以强化学习算法发展为主线, 综述该算法在单车自动驾驶决策领域的深入应用 …
要点问题. 本文以强化学习算法发展为主线, 综述该算法在单车自动驾驶决策领域的深入应用 …
An Autonomous Driving model with BEV-V2X Perception, Trajectory Prediction and Driving Planning in Complex Traffic Intersections
F Li, O Lin, K Gao, Y Li - arXiv preprint arXiv:2312.05104, 2023 - arxiv.org
The comprehensiveness of vehicle-to-everything (V2X) recognition enriches and holistically
shapes the global Birds-Eye-View (BEV) perception, incorporating rich semantics and …
shapes the global Birds-Eye-View (BEV) perception, incorporating rich semantics and …
State Constraints and Safety Consideration
SE Li - Reinforcement Learning for Sequential Decision and …, 2023 - Springer
Controlling a real-world system with state constraints has drawn increasing attention due to
practical needs, such as operating limits and safety guarantees. Equipping RL/ADP with the …
practical needs, such as operating limits and safety guarantees. Equipping RL/ADP with the …