ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios

TL;DR

ScenicRules introduces a comprehensive benchmark for autonomous driving that incorporates multi-objective, prioritized rules within formal environment scenarios, enabling better evaluation of system performance in complex traffic situations.

Contribution

The paper presents a novel benchmark combining multi-objective prioritized rules with formal Scenic scenarios, filling a gap in existing autonomous driving evaluation tools.

Findings

Aligns well with human driving judgments

Effectively exposes agent failures in prioritized objectives

Provides a versatile framework for diverse driving contexts

Abstract

Developing autonomous driving systems for complex traffic environments requires balancing multiple objectives, such as avoiding collisions, obeying traffic rules, and making efficient progress. In many situations, these objectives cannot be satisfied simultaneously, and explicit priority relations naturally arise. Also, driving rules require context, so it is important to formally model the environment scenarios within which such rules apply. Existing benchmarks for evaluating autonomous vehicles lack such combinations of multi-objective prioritized rules and formal environment models. In this work, we introduce ScenicRules, a benchmark for evaluating autonomous driving systems in stochastic environments under prioritized multi-objective specifications. We first formalize a diverse set of objectives to serve as quantitative evaluation metrics. Next, we design a Hierarchical Rulebook framework that encodes multiple objectives and their priority relations in an interpretable and adaptable manner. We then construct a compact yet representative collection of scenarios spanning diverse driving contexts and near-accident situations, formally modeled in the Scenic language. Experimental results show that our formalized objectives and Hierarchical Rulebooks align well with human driving judgments and that our benchmark effectively exposes agent failures with respect to the prioritized objectives. Our benchmark can be accessed at https://github.com/BerkeleyLearnVerify/ScenicRules/.