Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles

2019-07-10 · Konstantinos Makantasis, Maria Kontorinaki, Ioannis Nikolos

In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of our knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles' behavior on the overall traffic flow are presented.

📄 PDF Abstract BibTeX arXiv:1907.05246

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous VehiclesDeep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning

2023-12-27 · Di Chen, Hao Li, Zhicheng Jin, Huizhao Tu 외

Autonomous vehicles (AVs) have the potential to prevent accidents caused by drivers errors and reduce road traffic risks. Due to the nature of heavy vehicles, whose collisions cause more serious crashes, the weights of v…

Autonomous Driving Autonomous Vehicles Decision Making Deep Reinforcement Learning

Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving

2019-11-26 · Rupert Mitchell, Jenny Fletcher, Jacopo Panerati, Amanda Prorok

Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based met…

Autonomous Driving Autonomous Vehicles Deep Reinforcement Learning Mixed Reality+3

Model-free Deep Reinforcement Learning for Urban Autonomous Driving

2019-04-20 · Jianyu Chen, Bodi Yuan, Masayoshi Tomizuka

Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in s…

Autonomous Driving Decision Making Deep Reinforcement Learning reinforcement-learning+2

Robust Driving Policy Learning with Guided Meta Reinforcement Learning

2023-07-19 · Kanghoon Lee, Jiachen Li, David Isele, Jinkyoo Park 외

Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the …

Autonomous Navigation Deep Reinforcement Learning Meta Reinforcement Learning reinforcement-learning+1

Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving

2025-02-28 · Nanshan Deng, Weitao Zhou, Bo Zhang, Junze Wen 외

Current autonomous vehicles operate primarily within limited regions, but there is increasing demand for broader applications. However, as models scale, their limited capacity becomes a significant challenge for adapting…

Autonomous Driving Autonomous Vehicles Graph Neural Network