SU Yimin (苏奕敏), WANG Lin∗ (王 琳) . Integrated Framework for Test and Evaluation of Autonomous Vehicles[J]. Journal of Shanghai Jiaotong University(Science), 2021 , 26(5) : 699 -712 . DOI: 10.1007/s12204-021-2360-y

[1] HUANG H, ZHENG X, YANG Y, et al. An integrated architecture for intelligence evaluation of automated vehicles [J]. Accident Analysis & Prevention, 2020, 145: 105681. [2] HUANG H M, PAVEK K, NOVAK B, et al. A frame-work for autonomy levels for unmanned systems (AL-FUS) [C]//AUVSI’s Unmanned Systems North Amer-ica. Baltimore: NIST, 2005: 1-9. [3] DARPA. Urban challenge rules [DB/OL]. (2007-10-27)[2020-11-20]. https://www.grandchallenge.org/ grand-challenge/docs/Urban Challenge Rules 102707.pdf. [4] SUN Y, CHEN H. Research on test and evaluation of unmanned ground vehicles [J]. Acta Armamentarii, 2015, 36(6): 978-986 (in Chinese). [5] SUN Y, XIONG G, CHEN H. Evaluation of the in-telligent behaviors of unmanned ground vehicles based on fuzzy-EAHP scheme [J]. Automotive Engineering, 2014, 36(1): 22-27 (in Chinese). [6] SUN Y. Quantitative evaluation of intelligence levels for unmanned ground vehicles [D]. Beijing: Beijing In-stitute of Technology, 2014 (in Chinese). [7] XIONG G M, GAO L, WU S B, et al. Intelligent be-haviors and test and evaluation for unmanned ground vehicles [M]. Beijing: Beijing Institute of Technology Press, 2015 (in Chinese). [8] SON T D, BHAVE A, VAN DER AUWERAER H. Simulation-based testing framework for autonomous driving development [C]//2019 IEEE International Conference on Mechatronics(ICM ). Ilmenau: IEEE, 2019: 576-583. [9] WANG G, DENG W, ZHANG S, et al. A comprehen-sive testing and evaluation approach for autonomous vehicles [J]. SAE Technical Paper, 2018: 2018-01-0124. [10] WENG B, RAO S J, DEOSTHALE E, et al. Model predictive instantaneous safety metric for evaluation of automated driving systems [C]//IEEE Intelligent Ve-hicles Symposium (IV ). Las Vegas: IEEE, 2020: 1899-1906. [11] LI L, HUANG W, LIU Y, et al. Intelligence testing for autonomous vehicles: A new approach [J]. IEEE Transactions on Intelligent Vehicles, 2016, 1(2): 158-166. [12] FENG S, FENG Y, YU C, et al. Testing scenario li-brary generation for connected and automated vehi-cles, part I: Methodology [J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(3): 1573-1582. [13] FENG S, FENG Y, SUN H, et al. Testing scenario library generation for connected and automated vehi-cles, part II: case studies [J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(9): 5635-5647. [14] SAE International. Taxonomy and de?nitions for terms related to driving automation systems for on-road motor vehicles: J3016 [R]. Warrendale: SAE, 2018. [15] YU Z, XING X, CHEN J. Review on automated vehi-cle testing technology and its application [J]. Journal of Tongji University (Natural Science), 2019, 47(4): 540- 547 (in Chinese). [16] PEGASUS. PEGASUS joint project [DB/OL].(2019-05-14) [2020-11-20]. http://www.pegasuspro-jekt.de/en/. [17] MENZEL T, BAGSCHIK G, MAURER M. Scenar-ios for development, test and validation of automated vehicles [C]//2018 IEEE Intelligent Vehicles Sympo-sium(IV ). Changshu: IEEE, 2018: 1821-1827. [18] WATANABE H, TOBISCH L, ROST J, et al. Sce-nario mining for development of predictive safety func-tions [C]//2019 IEEE International Conference on Ve-hicular Electronics and Safety(ICVES ). Cairo: IEEE, 2019: 1-7. [19] HighD Dataset. HighD dataset [DB/OL]. [2020-11-20]. https: //www.highd-dataset.com/. [20] DATA.GOV. Safety pilot model deployment data [DB/OL]. (2020-08-21) [2020-11-20]. https://catalog. data.gov/dataset/safety-pilot-model-deployment-data. [21] UMTRI. Safety pilot model deployment. [DB/OL].[2020-11-20]. http://safetypilot.umtri.umich.edu/. [22] RAHMAN M S, ABDEL-ATY M, LEE J, et al. Safety bene?ts of arterials’ crash risk under connected and automated vehicles [J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 354-371. [23] CHEN J, LI R, XING X, et al. Survey on intelli-gence evaluation of autonomous vehicles [J]. Journal of Tongji University (Natural Science), 2019, 47(12): 1785-1790 (in Chinese). [24] ZHAO Y N, MENG K W, GAO L. The entropy-cost function evaluation method for unmanned ground vehicles [J]. Mathematical Problems in Engineering, 2015, 2015:1-6. [25] HAYWARD J C. Near-miss determination through use of a scale of danger [C]//51st Annual Meeting of the Highway Research Board. Washington, DC: Highway Research Board, 1972: 24-34. [26] Standardization Administration. Intelligent trans-portation systems: Forward vehicle collision warning systems: Performance requirements and test proce-dures: GB/T 33577—2017 [S]. Beijing: Standards Press of China, 2017 (in Chinese). [27] MINDERHOUD M M, BOVY P H L. Extended time-to-collision measures for road tra?c safety assessment [J]. Accident Analysis & Prevention, 2001, 33(1): 89-97. [28] BAGDADI O, VA′ RHELYI A. Jerky driving: An indi-cator of accident proneness? [J]. Accident Analysis & Prevention, 2011, 43(4): 1359-1363. [29] BELLEM H, SCHOENENBERG T, KREMS J F, et al. Objective metrics of comfort: developing a driving style for highly automated vehicles [J]. Transportation Research Part F: Tra?c Psychology and Behaviour, 2016, 41: 45-54. [30] GUO Y. Comprehensive evaluation theory and method [M]. Beijing: Science Press, 2002 (in Chinese). [31] XIA Q, LIU K, HUANG L, et al. Study of compre-hensive evaluation for L2 automated vehicles on ?eld test [C]//2019 3rd Conference on Vehicle Control and Intelligence (CVCI ). Hefei: IEEE, 2019: 1-6. [32] WANG X H, LI T J, DING L L. Evaluation theory and technology of complex large-scale systems [M]. Jinan: Shandong University Press, 2010 (in Chinese).