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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Egocentric abstractions for modeling and safety verification of distributed cyber-physical systems
Modeling is a significant piece of the puzzle in achieving safety certificates for distributed IoT and cyberphysical systems. From smart home devices to connected and autonomous vehicles, several modeling challenges like dynamic membership of participants and complex interaction patterns, span across application domains. Modeling multiple interacting vehicles can become unwieldy and impractical as vehicles change relative positions and lanes. In this paper, we present an egocentric abstraction for succinctly modeling local interactions among an arbitrary number of agents around an ego agent. These models abstract away the detailed behavior of the other agents and ignore present but physically distant agents. We show that this approach can capture interesting scenarios considered in the responsibility sensitive safety (RSS) framework for autonomous vehicles. As an illustration of how the framework can be useful for analysis, we prove safety of several highway driving scenarios using egocentric models. The proof technique also brings to the forefront the power of a classical verification approach, namely, inductive invariant assertions. We discuss possible generalizations of the analysis to other scenarios and applications.
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- Award ID(s):
- 1918531
- NSF-PAR ID:
- 10298749
- Date Published:
- Journal Name:
- 2021 IEEE Security and Privacy Workshops (SPW)
- Page Range / eLocation ID:
- 268 to 276
- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SPW53761.2021.00046
