Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques
By Ding Zhao, Henry Lam, Huei Peng, Shan Bao, David J. LeBlanc, Kazutoshi Nobukawa, Christopher S. Pan. IEEE transactions on intelligent transportation systems (Print), 2016
404 citations recorded by the source corpus.
Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs.
Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/f0d25be37a7baccc89ff64ddbadc0be4685ff8a4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
In Control and Systems Engineering
Citations in the corpus
- Dense reinforcement learning for safety validation of autonomous vehicles (2023, background): “Yet, existing scenario-based approaches 12–17 can mainly be applied to short scenario segments with limited background road users (see Supplementary Information for more discussions).”
- A Survey of Vehicle to Everything (V2X) Testing (2019, methodology): “Some technologies such as importance sampling are used to accelerate the testing process [97].”