Parallel and Multi-Objective Falsification with Scenic and VerifAI

Falsification has emerged as an important tool for simulation-based verification of autonomous systems. In this paper, we present extensions to the Scenic scenario specification language and VerifAI toolkit that improve the scalability of sampling-based falsification methods by using parallelism and extend falsification to multi-objective specifications. We first present a parallelized framework that is interfaced with both the simulation and sampling capabilities of Scenic and the falsification capabilities of VerifAI, reducing the execution time bottleneck inherently present in simulation-based testing. We then present an extension of VerifAI's falsification algorithms to support multi-objective optimization during sampling, using the concept of rulebooks to specify a preference ordering over multiple metrics that can be used to guide the counterexample search process. Lastly, we evaluate the benefits of these extensions with a comprehensive set of benchmarks written in the Scenic language

Towards Assume-Guarantee Profiles for Autonomous Vehicles

Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this practice, we propose a systematic procedure for generating a set of supervisory specifications for self-driving cars that are 1) associated with a distributed assume-guarantee structure and 2) characterizable by the notion of consistency and completeness. Besides helping autonomous vehicles make better decisions on the road, the assume-guarantee contract structure also helps address the notion of blame when undesirable events occur. We give several game-theoretic examples to demonstrate applicability of our framework

Exceptional Driving Principles for Autonomous Vehicles

Public expectations for automated vehicles span a broad range, from mobility for passengers, to road user safety, to compliance with the traffic code. In most ordinary situations, these expectations can be satisfied simultaneously. But these various expectations can also lead to exceptional scenarios where certain objectives, such as those related to safety, are in tension with road rules. Exceptional driving scenarios challenge motion planning algorithms in automated vehicles to find solutions that are legally grounded, ethically sound, and technically feasible. The general public’s familiarity with exceptional driving scenarios comes from the classic Trolley Car problem in philosophy, asking who should live and who should die in an unavoidable collision. These discussions tend to take a consequentialist view by framing the ethical action as the one that achieves the best outcome. By taking a different perspective that views driving as a social contract, the AV\u27s ethical obligations are limited to meeting the duty of care owed to other road users. With this perspective, the existing legal system in the US provides a framework for choosing appropriate behaviors in exceptional driving cases and for answering the Trolley Car problem. This work outlines principles that prioritize care for humans, respect the authority of human-defined traffic law, and ensure that the vehicle avoids decisions that introduce unreasonable risks. Developing AVs that can legally and ethically negotiate exceptional driving scenarios is simply a matter of translating the principles into engineering requirements with no need for new laws or endless philosophical debate

Towards Assume-Guarantee Profiles for Autonomous Vehicles

Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this practice, we propose a systematic procedure for generating a set of supervisory specifications for self-driving cars that are 1) associated with a distributed assume-guarantee structure and 2) characterizable by the notion of consistency and completeness. Besides helping autonomous vehicles make better decisions on the road, the assume-guarantee contract structure also helps address the notion of blame when undesirable events occur. We give several game-theoretic examples to demonstrate applicability of our framework

Normative Diagrams as a Tool for Representing Legal Systems

The paper at hand introduces and discusses a diagrammatic method to represent legal norms frst developed by the second author. It is shown how this method can be used to represent not only norms and argument forms originating from classic legal methodology (legal subsumption, analogy, appeal to the contrary), but also more complex legal-theoretical phenomena, especially legal antinomies. Beyond its didactic virtues, the diagram is a useful theoretical tool for investigating how norms interact with each other and how singular actions can be considered as satisfying or violating a given norm