An integrated architecture for intelligence evaluation of automated vehicles. (September 2020)

• Record Type: Journal Article
• Authors: Huang, Heye; Zheng, Xunjia; Yang, Yibin; Liu, Jinxin; Liu, Wenjun; Wang, Jianqiang
• Is Part Of: Accident analysis and prevention. Volume 145(2020)
• Publication Date: 2020-09
• Subjects: Intelligence evaluation -- Driving action -- Test field -- Intelligence degree -- Automated vehicles; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105681
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Proposes a framework for quantitatively evaluating the intelligence of automated vehicles. Develops the symmetric intelligence evaluation index based on principle of least action. Completes evaluation process by testing different scenarios, including normal driving and high-risk scenarios. Abstract: Increasing automation calls for evaluating the effectiveness and intelligence of automated vehicles. This paper proposes a framework for quantitatively evaluating the intelligence of automated vehicles. Firstly, we establish the evaluation environment for automated vehicles including test field, test task, and evaluation index. The test tasks include the single vehicle decision-making (turning, lane-changing, overtaking, etc.) and the maneuver execution of multi-vehicle interaction (obstacle avoidance, trajectory optimization, etc.). The intelligence evaluation index is the action amount of driving process considering the safety, efficiency, rationality and comfort. Then, we calculate the actual action amount of the automated vehicle in different scenarios in the test field. Finally, the least action calculated theoretically corresponds to the highest intelligence degree of the automated vehicle, and is employed as a standard to quantify the performance of other tested automated vehicles. The effectiveness of this framework is verified with two naturalistic driving datasets that contain the normal driving scenarios and high-risk scenarios. Specifically, the naturalistic lane-changing data filters 40, 416 frames and 179 similar lane-changing trajectories. Compared with the lane-changing behavior of a large number of drivers, experimental results verify that the proposed algorithm can achieve the intelligence degree of drivers in the lane change scenario. Meanwhile, in 253 reconstructed high-risk scenarios, the intelligent risk avoidance ability of the proposed intelligence degree evaluation algorithm can be verified by comparing with the driver behavior and TTC algorithm. These experimental results show that the proposed framework can effectively quantify intelligence and evaluate the performance of automated vehicles under various scenarios.