Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment
By Shuo Feng, Xintao Yan, Haowei Sun, Yiheng Feng, Henry X. Liu. Nature Communications, 2021
445 citations recorded by the source corpus.
Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. Tests for autonomous vehicles are usually made in the naturalistic driving environment where safety-critical scenarios are rare. Feng et al. propose a testing approach combining naturalistic and adversarial environment which allows to accelerate testing process and detect dangerous driving events.
Subfields: Artificial Intelligence, Automotive Engineering
View Paper: https://www.semanticscholar.org/paper/fa1298fb839ad7e968bd149f702a5c7edbf46ced
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Citations in the corpus
- Dense reinforcement learning for safety validation of autonomous vehicles (2023, background): “In our previous study 14 , we found that the curse of dimensionality issue could be addressed theoretically by sparse adversarial control to the naturalistic distribution.”