Abstract
With the rapid and wide implementation of adaptive cruise control system (ACC), the testing and evaluation method becomes an important question. Based on the human driver behavior characteristics extracted from naturalistic driving studies (NDS), this paper proposed the testing and evaluation method for ACC systems, which considers safety and human-like at the same time. Firstly, usage scenarios of ACC systems are defined and test scenarios are extracted and categorized as safety test scenarios and human-like test scenarios according to the collision likelihood. Then, the characteristic of human driving behavior is analyzed in terms of time to collision and acceleration distribution extracted from NDS. According to the dynamic parameters distribution probability, the driving behavior is divided into safe, critical, and dangerous behavior regarding safety and aggressive and normal behavior regarding human-like according to different quantiles. Then, the baselines for evaluation are designed and the weights of different scenarios are determined according to exposure frequency, resulting in a comprehensive evaluation method. Finally, an ACC system is tested in the selected test scenarios and evaluated with the proposed method. The tested vehicle finally got a safety score of 0.9496 (full score: 1) and a human-like score as fail. The results revealed the tested vehicle has a remarkably different driving pattern to human drivers, which may lead to uncomfortable ride experience and user-distrust of the system.
Highlights
Advanced Driver Assistance Systems (ADAS) are drawing increasing attention due to their potential in enhancing traffic safety, reducing driving workload and improving traffic efficiency
On a flat and straight road, the leading vehicle is controlled by an experienced driver according to the scenario description and the host vehicle turns on the adaptive cruise control system (ACC) system for motion control
This paper proposed an ACC system testing and evaluation method based on human driver characteristics generated from naturalistic driving data, including testing scenarios and testing result evaluation method
Summary
Advanced Driver Assistance Systems (ADAS) are drawing increasing attention due to their potential in enhancing traffic safety, reducing driving workload and improving traffic efficiency. With wide studies on the control strategies of ADAS like adaptive cruise control system (ACC) [1,2], lane-keeping system (LKS) [3], automated emergency braking system (AEB) [4], etc., the functionalities of such systems are well studied and qualified. It follows that improving the anthropomorphism should be incorporated into the development of these systems [5]. With the development of human-like ADAS, there raises the need for a testing and evaluation method considering human-like behavior
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