A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Reinforcement learning‐based bird‐view automated vehicle control to avoid crossing traffic
This paper presents an innovative bird‐view control framework for connected automated vehicles (CAV). Most recently tested automated vehicles are based on sensing systems equipped on the car body, which require the self‐driving policy to be robust and adaptive to various environmental uncertainties. Inspired by the vehicle to infrastructure technologies, the self‐driving technology can also be achieved through the communication between road infrastructure and the vehicle, where sensors are mainly installed on the road in a high position, which can collect traffic information from a bird‐view. To this end, we developed a fusion‐based Q‐learning method to yield an optimal bird‐view control policy for a CAV on a multi‐lane road. With our control policy, the CAV can drive smartly under complicated traffic environment, interacting with leading vehicles and crossing traffic simultaneously. A series of case studies show our CAV control policy is string stable and can avoid collisions under various scenarios.
Reinforcement learning‐based bird‐view automated vehicle control to avoid crossing traffic
This paper presents an innovative bird‐view control framework for connected automated vehicles (CAV). Most recently tested automated vehicles are based on sensing systems equipped on the car body, which require the self‐driving policy to be robust and adaptive to various environmental uncertainties. Inspired by the vehicle to infrastructure technologies, the self‐driving technology can also be achieved through the communication between road infrastructure and the vehicle, where sensors are mainly installed on the road in a high position, which can collect traffic information from a bird‐view. To this end, we developed a fusion‐based Q‐learning method to yield an optimal bird‐view control policy for a CAV on a multi‐lane road. With our control policy, the CAV can drive smartly under complicated traffic environment, interacting with leading vehicles and crossing traffic simultaneously. A series of case studies show our CAV control policy is string stable and can avoid collisions under various scenarios.
Reinforcement learning‐based bird‐view automated vehicle control to avoid crossing traffic
Wang, Yipei (author) / Hou, Shuaikun (author) / Wang, Xin (author)
Computer‐Aided Civil and Infrastructure Engineering ; 36 ; 890-901
2021-07-01
12 pages
Article (Journal)
Electronic Resource
English
Automated Adaptive Traffic Corridor Control Using Reinforcement Learning: Approach and Case Studies
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Automated Adaptive Traffic Corridor Control Using Reinforcement Learning: Approach and Case Studies
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