Counterexample-Guided Safety Contracts for Autonomous Driving

DeCastro, Jonathan A.; Liebenwein, Lucas; Vasile, Cristian-Ioan; Tedrake, Russ; Karaman, Sertaç; Rus, Daniela

doi:10.1007/978-3-030-44051-0_54

Cited by 5 publications

(12 citation statements)

References 29 publications

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“…Decastro et al [8] define "impeding the traffic flow" as going below a speed difference threshold v ego − v. While they define v to be the average speed of the surrounding vehicles, it might be useful to damp this signal.…”

Section: A Speedmentioning

confidence: 99%

“…Here, the rules are checked on potential predicted outcomes [4,6,7]. Second, the safety community, which has tried to establish contracts consisting of a set of rules, which every vehicle should adhere to to prove safety [8,14]. These approaches generally rely on inter-vehicle communication.…”

Section: Related Workmentioning

confidence: 99%

“…No Stopping: On motorways and motor roads, "stopping is prohibited, including on verges" [ §18 (8) StVO].…”

Section: A Speedmentioning

confidence: 99%

“…Previous works have identified Linear Temporal Logic (LTL) as a suitable formal language to specify traffic rules [4][5][6]. Other works have used inequality constraints based on real numbers to formalize traffic rules [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Formalizing Traffic Rules for Machine Interpretability

Esterle

Gressenbuch

Knoll

2020

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)

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Autonomous vehicles need to be designed to abide by the same rules that humans follow. This is challenging, because traffic rules are fuzzy and not well defined, making them incomprehensible to machines. Satisfaction cannot be incorporated in a planning component without proper formalization, nor can it be monitored and verified during simulation or testing. However, no research work has provided a consistent set of machine-interpretable traffic rules for a given operational driving domain. In this paper, we propose a methodology for the legal study and formalization of traffic rules in a formal language. We use Linear Temporal Logic as a formal specification language to describe temporal behaviors, capable of capturing a wide range of traffic rules. We contribute a formalized set of traffic rules for dual carriageways and evaluate the effectiveness of our formalized rules on a public dataset.

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Section: A Speedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…No Stopping: On motorways and motor roads, "stopping is prohibited, including on verges" [ §18 (8) StVO].…”

Section: A Speedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Formalizing Traffic Rules for Machine Interpretability

Esterle

Gressenbuch

Knoll

2020

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)

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“…Satisfying multi-agent traffic rules has been realized by establishing contracts between vehicles [4]. However, their approach is used for deriving requirements during the design phase, not for penalizing or restricting actions during planning.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning

Esterle¹,

Gressenbuch²,

Knoll³

2020

Preprint

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Autonomous vehicles need to abide by the same rules that humans follow. Some of these traffic rules may depend on multiple agents or time. Especially in situations with traffic participants that interact densely, the interactions with other agents need to be accounted for during planning. To study how multi-agent and time-dependent traffic rules shall be modeled, a framework is needed that restricts the behavior to ruleconformant actions during planning, and that can eventually evaluate the satisfaction of these rules. This work presents a method to model the conformance to traffic rules for interactive behavior planning and to test the ramifications of the traffic rule formulations on metrics such as collision, progress, or rule violations. The interactive behavior planning problem is formulated as a dynamic game and solved using Monte Carlo Tree Search, for which we contribute a new method to integrate history-dependent traffic rules into a decision tree. To study the effect of the rules, we treat it as a multi-objective problem and apply a relaxed lexicographical ordering to the vectorized rewards. We demonstrate our approach in a merging scenario. We evaluate the effect of modeling and combining traffic rules to the eventual compliance in simulation. We show that with our approach, interactive behavior planning while satisfying even complex traffic rules can be achieved. Moving forward, this gives us a generic framework to formalize traffic rules for autonomous vehicles.

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