Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios
D Chen, Y Gong, X Yang - arXiv preprint arXiv:2404.19087, 2024 - arxiv.org
Existing Advanced Driver Assistance Systems primarily focus on the vehicle directly ahead,
often overlooking potential risks from following vehicles. This oversight can lead to …
often overlooking potential risks from following vehicles. This oversight can lead to …
Lateral Motion Control for Obstacle Avoidance in Autonomous Driving Based on Deep Reinforcement Learning
To assist autonomous vehicles in confronting obstacle collision avoidance situations, a
model-free lateral motion control method is proposed for vehicle collision avoidance based …
model-free lateral motion control method is proposed for vehicle collision avoidance based …
Autonomous emergency steering using deep reinforcement learning for advanced driver assistance system
M Yoshimura, G Fujimoto, A Kaushik… - 2020 59th Annual …, 2020 - ieeexplore.ieee.org
Although automobile technology has continuously evolved in recent years, there are still
many traffic accidents all over the world. In order to reduce the number of traffic accidents …
many traffic accidents all over the world. In order to reduce the number of traffic accidents …
[HTML][HTML] Towards robust car-following based on deep reinforcement learning
One of the biggest challenges in the development of learning-driven automated driving
technologies remains the handling of uncommon, rare events that may have not been …
technologies remains the handling of uncommon, rare events that may have not been …
Collision Avoidance of Autonomous Vehicles with E-bike at Un-signalized Occluded Intersections Based on Reinforcement Learning
Un-signalized occluded intersections are residential road intersections with narrow lanes
and surrounding buildings, which are prone to traffic accidents. This work uses deep …
and surrounding buildings, which are prone to traffic accidents. This work uses deep …
A rear anti-collision decision-making methodology based on deep reinforcement learning for autonomous commercial vehicles
W Hu, X Li, J Hu, X Song, X Dong, D Kong… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Driving decision-making determines the safety and rationality of autonomous commercial
vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision …
vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision …
Deep reinforcement learning with enhanced safety for autonomous highway driving
In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …
The proposed framework leverages merits of both rule-based and learning-based …
Combined variable speed limit and lane change guidance for secondary crash prevention using distributed deep reinforcement learning
C Peng, C Xu - Journal of Transportation Safety & Security, 2022 - Taylor & Francis
The primary objective of this paper is to develop a combined variable speed limit (VSL) and
lane change guidance (LCG) controller to prevent secondary crashes (SCs) and improve …
lane change guidance (LCG) controller to prevent secondary crashes (SCs) and improve …
Vehicles control: Collision avoidance using federated deep reinforcement learning
In the face of growing urban populations and the escalating number of vehicles on the
roads, managing transportation efficiently and ensuring safety have become critical …
roads, managing transportation efficiently and ensuring safety have become critical …
Addressing crash-imminent situations caused by human driven vehicle errors in a mixed traffic stream: a model-based reinforcement learning approach for CAV
It is anticipated that the era of fully autonomous vehicle operations will be preceded by a
lengthy" Transition Period" where the traffic stream will be mixed, that is, consisting of …
lengthy" Transition Period" where the traffic stream will be mixed, that is, consisting of …
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