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ACCELERATED EVALUATION OF AUTOMATED VEHICLES IN LANE CHANGE SCENARIOS

机译:车道变化场景中自动车辆的加速评估

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It is important to rigorously and comprehensively evaluate the safety of Automated Vehicles (AVs) before their production and deployment. A popular AV evaluation approach is Naturalistic-Field Operational Test (N-FOT) which tests prototype vehicles directly on public roads. Due to the low exposure to safety-critical scenarios, N-FOTs is time-consuming and expensive to conduct. Computer simulations can be used as an alternative to N-FOTs, especially in terms of generating motions of the surrounding traffic. In this paper, we propose an accelerated evaluation approach for AVs. Human-controlled vehicles (HVs) were modeled as disturbance to AVs based on data extracted from the Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behavior, which amplifies riskier testing scenarios while reserves its statistical information so that the safety benefits of AV in non-accelerated cases can be accurately estimated. An AV model based on a production vehicle was tested. Results show that the proposed method can accelerate the evaluation process by at least 100 times.
机译:在生产和部署自动驾驶汽车之前,必须严格,全面地评估其安全性。一种流行的视音频评估方法是自然主义现场操作测试(N-FOT),可直接在公共道路上测试原型车辆。由于对安全至关重要的场景的暴露程度较低,因此N-FOT既耗时又昂贵。可以将计算机模拟用作N-FOT的替代方法,尤其是在生成周围交通运动方面。在本文中,我们提出了一种针对AV的加速评估方法。基于从安全飞行员模型部署计划中提取的数据,将人为控制的车辆(HV)建模为对AV的干扰。切入场景是基于收集的驾驶员行为的偏斜统计信息生成的,这放大了风险更高的测试场景,同时保留了其统计信息,以便可以准确地估计AV在非加速情况下的安全性。测试了基于生产车辆的AV模型。结果表明,所提方法至少可以使评价过程加快100倍。

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