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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Multi-Agent Trajectory Optimization Against Plan-Deviation Attacks using Co-Observations and Reachability Constraints
In this paper, we focus on using path planning and inter-agent measurements to improve the security of multi-robot systems against possible takeovers from cyber-attackers. We build upon recent trajectory optimization approaches where introspective measurement capabilities of the robots are used in an co-observation schedule to detect deviations from the preordained routes. This paper proposes additional constraints that can be incorporated in the previous trajectory optimization algorithm based on Alternating Direction Method of Multipliers (ADMM). The new constraints provide guarantees that a compromised robot cannot reach a designed safety zone between observations despite adversarial movement by the attacker. We provide a simulation showcasing the new components of the formulation in a multi-agent map exploration task with several safety zones.
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- Award ID(s):
- 1932162
- NSF-PAR ID:
- 10332619
- Date Published:
- Journal Name:
- IEEE International Conference on Decision and Control
- Page Range / eLocation ID:
- 241 to 247
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC45484.2021.9683465
