arXiv:2503.08936v3 Announce Type: replace-cross Abstract: Scenario-based testing with driving simulators is extensively used to identify failing conditions of automated driving assistance systems (ADAS). However, existing studies have shown that repeated test execution in the same as well as in distinct simulators can yield different outcomes, which can be attributed to sources of flakiness or different implementations of the physics. In this paper, we present MultiSim, a novel approach to multi-simulation ADAS testing based on a search-based testing approach that leverages an ensemble of simu
The increasing complexity of autonomous driving systems requires more robust and reliable testing methodologies, pushing research into multi-simulator approaches to address inconsistencies and improve verification.
Ensuring the trustworthiness of autonomous driving systems is critical for their societal adoption and regulatory approval, with simulator ensembles offering a path to more reliable validation.
The development and testing of autonomous driving software will increasingly incorporate ensemble simulation techniques to improve the accuracy and reliability of system validation, potentially leading to faster deployment cycles.
- · Autonomous vehicle developers
- · Simulation software providers
- · AI safety and testing firms
- · Consumers of autonomous driving systems
- · Companies relying solely on single-simulator testing
- · Less robust testing methodologies
Improved reliability and safety metrics for autonomous driving systems.
Accelerated regulatory approval and wider commercial deployment of self-driving cars.
Enhanced public trust in AI-driven mobility solutions, potentially shifting transportation paradigms faster than anticipated.
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Read at arXiv cs.AI