Encoding Integrated Decision and Control for Autonomous Driving with
Mixed Traffic Flow
- URL: http://arxiv.org/abs/2110.12359v1
- Date: Sun, 24 Oct 2021 06:06:27 GMT
- Title: Encoding Integrated Decision and Control for Autonomous Driving with
Mixed Traffic Flow
- Authors: Yangang Ren, Jianhua Jiang, Jingliang Duan, Shengbo Eben Li, Dongjie
Yu, Guojian Zhan
- Abstract summary: Reinforcement learning (RL) has been widely adopted to make intelligent driving policy in autonomous driving.
This paper proposes the encoding integrated decision and control (E-IDC) to handle complicated driving tasks with mixed traffic flows.
- Score: 5.7440882048331705
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reinforcement learning (RL) has been widely adopted to make intelligent
driving policy in autonomous driving due to the self-evolution ability and
humanoid learning paradigm. Despite many elegant demonstrations of RL-enabled
decision-making, current research mainly focuses on the pure vehicle driving
environment while ignoring other traffic participants like bicycles and
pedestrians. For urban roads, the interaction of mixed traffic flows leads to a
quite dynamic and complex relationship, which poses great difficulty to learn a
safe and intelligent policy. This paper proposes the encoding integrated
decision and control (E-IDC) to handle complicated driving tasks with mixed
traffic flows, which composes of an encoding function to construct driving
states, a value function to choose the optimal path as well as a policy
function to output the control command of ego vehicle. Specially, the encoding
function is capable of dealing with different types and variant number of
traffic participants and extracting features from original driving observation.
Next, we design the training principle for the functions of E-IDC with RL
algorithms by adding the gradient-based update rules and refine the safety
constraints concerning the otherness of different participants. The
verification is conducted on the intersection scenario with mixed traffic flows
and result shows that E-IDC can enhance the driving performance, including the
tracking performance and safety constraint requirements with a large margin.
The online application indicates that E-IDC can realize efficient and smooth
driving in the complex intersection, guaranteeing the intelligence and safety
simultaneously.
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