Scalable Multiagent Driving Policies For Reducing Traffic Congestion

by   Jiaxun Cui, et al.

Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed traffic scenarios with both AVs and human-driven vehicles, a small fraction of AVs executing a controlled multiagent driving policy can mitigate congestion. In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We start by showing that a congestion metric used by past research is manipulable in open road network scenarios where vehicles dynamically join and leave the road. We then propose using a different metric that is robust to manipulation and reflects open network traffic efficiency. Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles). Additionally, our modular transfer learning approach saves up to 80 collection on key locations in the network. Finally, we show for the first time a distributed multiagent policy that improves congestion over human-driven traffic. The distributed approach is more realistic and practical, as it relies solely on existing sensing and actuation capabilities, and does not require adding new communication infrastructure.


page 2

page 4


Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction

In most modern cities, traffic congestion is one of the most salient soc...

Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?

Connected autonomous vehicles (CAVs) can supplement the information from...

Learning How to Dynamically Route Autonomous Vehicles on Shared Roads

Road congestion induces significant costs across the world, and road net...

Towards Co-operative Congestion Mitigation

The effects of traffic congestion are widespread and are an impedance to...

Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars

The technological and scientific challenges involved in the development ...

AVARS – Alleviating Unexpected Urban Road Traffic Congestion using UAVs

Reducing unexpected urban traffic congestion caused by en-route events (...

PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems

Intelligent driving systems can be used to mitigate congestion through s...

Please sign up or login with your details

Forgot password? Click here to reset