ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios
arXiv:2602.16073v2 Announce Type: replace-cross 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-objectiv
The continuous development and deployment of autonomous driving systems necessitate more robust and comprehensive evaluation benchmarks, pushing the field to address multi-objective trade-offs and contextual rules.
This benchmark addresses critical limitations in current autonomous vehicle evaluation, moving beyond simple collision avoidance to incorporate complex real-world objectives and rules, which is crucial for public acceptance and safe deployment.
Autonomous vehicle development and testing will increasingly focus on multi-objective optimization and context-dependent rule interpretation, fostering more sophisticated and adaptable AI systems.
- · Autonomous vehicle developers
- · AI safety and ethics researchers
- · Simulation platform providers
- · Companies relying on simplistic AV testing
- · Developers ignoring complex rule systems
Improved safety and reliability of autonomous driving systems through more rigorous testing.
Accelerated development of AI systems capable of handling nuanced, multi-objective decision-making in real-world scenarios.
Potential for new regulatory frameworks that incorporate multi-objective and contextual compliance for autonomous systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI