Citation: | WANG Dan, ZHU Yueying, ZHANG Ce, LIN Ye. An Evaluation Model for Driver Takeover Performance Based on Multi-objective Indicators Representation[J]. Journal of Transport Information and Safety, 2024, 42(6): 55-63. doi: 10.3963/j.jssn.1674-4861.2024.06.006 |
[1] |
SAE International. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles: SAE J3016[S]. Warrendale, PA, USA: SAE, 2021.
|
[2] |
WEAVER B W, DELUCIA P R. A systematic review and meta-analysis of takeover performance during conditionally automated driving[J]. Human Factors, 2022, 64 (7) : 1227-1260. doi: 10.1177/0018720820976476
|
[3] |
DU N, ZHOU F, PULVER E M, et al. Predicting driver takeover performance in conditionally automated driving[J]. Accident Analysis & Prevention, 2020, 148: 105748.
|
[4] |
AGRAWAL S, PEETA S. Evaluating the impacts of driver' s pre-warning cognitive state on takeover performance under conditional automation[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 83: 80-98. doi: 10.1016/j.trf.2021.10.004
|
[5] |
WU C, WU H, LYU N, et al. Take-over performance and safety analysis under different scenarios and secondary tasks in conditionally automated driving[J]. IEEE Access, 2019(7) : 136924-136933.
|
[6] |
XU L, GUO L, GE P, et al. Effect of multiple monitoring requests on vigilance and readiness by measuring eye movement and takeover performance[J]. Transportation Research Part F: Psychology and Behaviour, 2022, 91: 179-190. doi: 10.1016/j.trf.2022.10.001
|
[7] |
WU Y, ABDEL-ATY M, PARK J, et al. Effects of real-time warning systems on driving under fog conditions using an empirically supported speed choice modeling framework[J]. Transportation Research Part C: Emerging Technologies, 2018, 86: 97-110. doi: 10.1016/j.trc.2017.10.025
|
[8] |
AYOUB J, DU N, YANG X J, et al. Predicting driver takeover time in conditionally automated driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7) : 9580-9589. doi: 10.1109/TITS.2022.3154329
|
[9] |
CAO Y, ZHOU F, PULVER E M, et al. Towards standardized metrics for measuring takeover performance in conditionally automated driving: a systematic review[C]. The Human Factors and Ergonomics Society Annual Meeting, Los Angeles: SAGE Publications, 2021.
|
[10] |
WU H, WU C, LYU N, et al. Does a faster takeover necessarily mean it is better? A study on the influence of urgency and takeover-request lead time on takeover performance and safety[J]. Accident Analysis & Prevention, 2022, 171: 106647.
|
[11] |
GOLD C, DAMBÖCK D, LORENZ L, et al. "Take over!" How long does it take to get the driver back into the loop?[C]. The Human Factors and Ergonomics Society Annual Meeting, 2013, 57 (1) : 1938-1942.
|
[12] |
SHINAR D, TRACTINSKY N, COMPTON R. Effects of practice, age, and task demands, on interference from a phone task while driving[J]. Accident Analysis & Prevention, 2005, 37 (2) : 315-326.
|
[13] |
KIM H J, YANG J H. Takeover requests in simulated partially autonomous vehicles considering human factors[J]. IEEE Transactionson Human-Machine Systems, 2017, 47 (5) : 735-740. doi: 10.1109/THMS.2017.2674998
|
[14] |
LI Q, WANG Z, WANG W, et al. An adaptive time budget adjustment strategy based on a takeover performance model for passive fatigue[J]. IEEE Transactions on Human-Machine Systems, 2021, 52 (5) : 1025-1035.
|
[15] |
JAROSCH O, BENGLER K. Rating of takeover performance in conditionally automated driving using an expert-rating system[C]. Advances in Human Aspects of Transportation, Orlando, Florida, USA: AHFE, 2018.
|
[16] |
NAUJOKS F, WIEDEMANN K, SCHÖMIG N, et al. Expert-based controllability assessment of control transitions from automated to manual driving[J]. MethodsX, 2018(5) : 579-592.
|
[17] |
RADLMAYR J, RATTER M, FELDHÜTTER A, et al. Take-overs in level 3 automated driving-proposal of the takeover performance score(tops)[C]. 20th Congress of the International Ergonomics Association (IEA 2018), Florence, Italy: IEA, 2018.
|
[18] |
LI Q, WANG Z, WANG W, et al. A human-centered comprehensive measure of takeover performance based on multiple objective metrics[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (4) : 4235-4250. doi: 10.1109/TITS.2022.3233623
|
[19] |
DU N, YANG X J, ZHOU F. Psychophysiological responses to takeover requests in conditionally automated driving[J]. Accident Analysis & Prevention, 2020, 148: 105804.
|
[20] |
LOUW T, KUO J, ROMANO R, et al. Engaging in NDRTs affects drivers' responses and glance patterns after silent automation failures[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 62: 870-882. doi: 10.1016/j.trf.2019.03.020
|
[21] |
COHEN J. Statistical power analysis for the behavioral science. [J]. Technometrics 1988, 31 (4) : 499-500.
|
[22] |
ERIKSSON A, STANTON N A. Takeover time in highly automated vehicles: noncritical transitions to and from manual control[J]. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2017, 59 (4): 689-705. doi: 10.1177/0018720816685832
|
[23] |
WAN J, WU C. The effects of lead time of take-over request and nondriving tasks on taking-over control of automated vehicles[J]. IEEE Transactions on Human-Machine Systems, 2018: 582-591.
|
[24] |
NAUJOKS F, HÖFLING S, PURUCKER C, et al. From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and takeover performance[J]. Accident Analysis & Prevention, 2018, 121: 28-42.
|
[25] |
王文军, 李清坤, 曾超, 等. 自动驾驶接管绩效的影响因素、模型与评价方法综述[J]. 中国公路学报, 2023, 36(9) : 202-224.
WANG W J, LI Q K, ZENG C, et al. Reviewo tfakeover performance of automated D riving: influencing factors, models, and evaluation methods[J]. China Journal of Highway and Transport, 2023, 36 (9) : 202-224. (in Chinese)
|