Connected Autonomous Vehicles (CAVs) are expected to enable reliable, efficient, and intelligent transportation
systems. Most motion planning algorithms for multi-agent systems implicitly assume that all vehicles/agents
will execute the expected plan with a small error and evaluate their safety constraints based on this fact. This
assumption, however, is hard to keep for CAVs since they may have to change their plan (e.g., to yield to
another vehicle) or are forced to stop (e.g., A CAV may break down). While it is desired that a CAV never gets
involved in an accident, it may be hit by other vehicles and sometimes, preventing the accident is impossible
(e.g., getting hit from behind while waiting behind the red light). Responsibility-Sensitive Safety (RSS) is a set
of safety rules that defines the objective of CAV to blame, instead of safety. Thus, instead of developing a CAV
algorithm that will avoid any accident, it ensures that the ego vehicle will not be blamed for any accident it is
a part of. Original RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road
scenarios, plus RSS rules do not prevent deadlock situations among vehicles. In this paper, we propose a new
formulation for RSS rules that can be applied to any driving scenario. We integrate the proposed RSS rules
with the CAV’s motion planning algorithm to enable cooperative driving of CAVs. We use Control Barrier
Functions to enforce safety constraints and compute the energy optimal trajectory for the ego CAV. Finally, to
ensure liveness, our approach detects and resolves deadlocks in a decentralized manner. We have conducted
different experiments to verify that the ego CAV does not cause an accident no matter when other CAVs
slow down or stop. We also showcase our deadlock detection and resolution mechanism using our simulator.
Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously
with the case that they are autonomous and connected.
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Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles
With the development of sensing and communica- tion technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based methodolo- gies are integrated into the control process of physical systems and demonstrate prominent performance in a wide array of CPS domains, such as connected autonomous vehicles (CAVs). However, it remains challenging to mathematically characterize the improvement of the performance of CAVs with commu- nication and cooperation capability. When each individual autonomous vehicle is originally self-interest, we can not assume that all agents would cooperate naturally during the training process. In this work, we propose to reallocate the system’s total reward efficiently to motivate stable cooperation among autonomous vehicles. We formally define and quantify how to reallocate the system’s total reward to each agent under the proposed transferable utility game, such that communication- based cooperation among multi-agents increases the system’s total reward. We prove that Shapley value-based reward reallocation of MARL locates in the core if the transferable utility game is a convex game. Hence, the cooperation is stable and efficient and the agents should stay in the coalition or the cooperating group. We then propose a cooperative policy learning algorithm with Shapley value reward reallocation. In experiments, compared with several literature algorithms, we show the improvement of the mean episode system reward of CAV systems using our proposed algorithm.
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- NSF-PAR ID:
- 10329063
- Date Published:
- Journal Name:
- 2022 IEEE International Conference on Robotics and Automation
- Page Range / eLocation ID:
- 8765 - 8771
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Connected Autonomous Vehicles (CAVs) are expected to enable reliable and efficient transportation systems. Most motion planning algorithms for multi-agent systems are not completely safe because they implicitly assume that all vehicles/agents will execute the expected plan with a small error. This assumption, however, is hard to keep for CAVs since they may have to slow down (e.g., to yield to a jaywalker) or are forced to stop (e.g. break down), sometimes even without a notice. Responsibility-Sensitive Safety (RSS) defines a set of safety rules for each driving scenario to ensure that a vehicle will not cause an accident irrespective of other vehicles' behavior. RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios. In addition, deadlock situations can happen that are not considered by the RSS. In this paper, we propose a generic version of RSS rules for CAVs that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV's motion planning algorithm to enable cooperative driving of CAVs. Our approach can also detect and resolve deadlocks in a decentralized manner. We have conducted experiments to verify that a CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously but not connected with the case that they are connected.more » « less
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Accepted Manuscript1.0
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