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Article

Study on Performance Testing and Evaluation of Adaptive Cruise Control Systems Based on a Self-Constructed Comprehensive Performance Evaluation Index Model

1
School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
2
Intelligent Testing and High-End Equipment of Automotive Power Systems, Shandong Province Engineering Research Center, Jinan 250357, China
3
Jinan Engineering Research Center of Automotive Equipment and Technology, Jinan 250357, China
4
Dezhou Xinlingzhi Testing Equipment Co., Ltd., Dezhou 251200, China
*
Author to whom correspondence should be addressed.
Machines 2026, 14(7), 827; https://doi.org/10.3390/machines14070827
Submission received: 28 May 2026 / Revised: 13 July 2026 / Accepted: 20 July 2026 / Published: 21 July 2026
(This article belongs to the Section Automation and Control Systems)

Abstract

Adaptive cruise control (ACC) performance is affected by multiple coupled factors, including safety margin, dynamic response, spacing regulation, target-transition behavior, and ride comfort. A single indicator is therefore insufficient for comprehensive ACC evaluation. This study proposes an adaptive cruise control comprehensive performance evaluation index model (ACC-CPEIM) for scenario-oriented ACC testing and diagnosis. The model links functional objectives, six typical ACC scenarios, measurable longitudinal indicators, hierarchical weights, and scenario-specific scoring rules into a unified evaluation chain. Time-domain response data are converted into scenario-level, criterion-level, and overall performance scores, while the results remain traceable to specific weak scenarios and performance dimensions. The proposed model was evaluated using a CarSim/Simulink co-simulation platform and further applied to vehicle-test data. The co-simulation results yielded an overall score of approximately 3.32 and identified weak acceleration-following response, insufficient spacing reserve during deceleration, and limited cut-in safety margin as the main limitations. The vehicle-test application produced an overall score of approximately 2.98 and showed that comfort and steady-state control were relatively stronger, whereas target-transition adaptability, safety margin, and dynamic response remained limiting dimensions. The results indicate that the ACC-CPEIM can provide quantitative, interpretable, and engineering-oriented support for ACC performance testing and diagnosis.
Keywords: adaptive cruise control; performance evaluation; comprehensive performance evaluation index model; scenario-based testing; hierarchical weighting; vehicle test; CarSim/Simulink co-simulation adaptive cruise control; performance evaluation; comprehensive performance evaluation index model; scenario-based testing; hierarchical weighting; vehicle test; CarSim/Simulink co-simulation

Share and Cite

MDPI and ACS Style

Zhang, H.; Huang, W.; Wang, Y.; Tian, X.; Fu, W.; Chu, R.; Qiu, F. Study on Performance Testing and Evaluation of Adaptive Cruise Control Systems Based on a Self-Constructed Comprehensive Performance Evaluation Index Model. Machines 2026, 14, 827. https://doi.org/10.3390/machines14070827

AMA Style

Zhang H, Huang W, Wang Y, Tian X, Fu W, Chu R, Qiu F. Study on Performance Testing and Evaluation of Adaptive Cruise Control Systems Based on a Self-Constructed Comprehensive Performance Evaluation Index Model. Machines. 2026; 14(7):827. https://doi.org/10.3390/machines14070827

Chicago/Turabian Style

Zhang, Hongtao, Wanyou Huang, Yan Wang, Xuesong Tian, Wenjun Fu, Ruixia Chu, and Fangyuan Qiu. 2026. "Study on Performance Testing and Evaluation of Adaptive Cruise Control Systems Based on a Self-Constructed Comprehensive Performance Evaluation Index Model" Machines 14, no. 7: 827. https://doi.org/10.3390/machines14070827

APA Style

Zhang, H., Huang, W., Wang, Y., Tian, X., Fu, W., Chu, R., & Qiu, F. (2026). Study on Performance Testing and Evaluation of Adaptive Cruise Control Systems Based on a Self-Constructed Comprehensive Performance Evaluation Index Model. Machines, 14(7), 827. https://doi.org/10.3390/machines14070827

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