Scenario Generation for Testing of Autonomous Driving Systems Using Real-World Failure Records
arXiv:2606.31131v1 Announce Type: new Abstract: To ensure safe on-road behavior, pre-deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial. Present day simulation based testing methods focus largely on mathematical models for efficient search of optimal scenarios, assuming a fixed scenario representation. On the other hand, real-world testing involves substantial manual effort to design scenario templates for testing. These templates represent distinct failure scenarios consisting of pre-deployment vehicle movements, map types, etc. Historical failure records
The increasing complexity of autonomous driving systems coupled with high safety demands necessitates more robust and efficient testing methodologies as deployment scales. This paper addresses that immediate need.
This development is crucial for ensuring the safety and accelerating the commercialization of autonomous driving, impacting regulatory frameworks, public acceptance, and the economic viability of AI-driven mobility. Reliable failure discovery is paramount for trust and scale.
Testing for autonomous driving systems can move beyond purely mathematical models or manual scenario design, integrating real-world failure data to create more realistic and effective failure-discovery scenarios, potentially accelerating development cycles and improving safety. This changes the 'how' of testing.
- · Autonomous Driving System Developers
- · Insurance Companies
- · Simulation Software Providers
- · Public Safety Advocates
- · Traditional Manual Testing Methods
- · Systems with Inefficient Scenario Generation
- · Unsafe Autonomous Vehicle Operators
More rigorous and efficient pre-deployment testing of autonomous vehicles leads to safer systems on public roads.
Accelerated development and higher safety standards drive faster adoption of autonomous driving technology in various sectors like logistics and public transport.
Reduced accident rates due to autonomous vehicles could free up significant societal resources currently allocated to accident response, medical care, and legal disputes, impacting urban planning and healthcare costs.
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Read at arXiv cs.AI