Volume 42 Issue 6
Dec.  2024
Turn off MathJax
Article Contents
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
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

An Evaluation Model for Driver Takeover Performance Based on Multi-objective Indicators Representation

doi: 10.3963/j.jssn.1674-4861.2024.06.006
  • Received Date: 2024-04-24
    Available Online: 2025-03-08
  • The takeover performance of drivers is of great significance for the safety, driving experience, and acceptance of conditionally automated vehicles. To study the impacts of driver behavior on takeover performance, a comprehensive evaluation representation index, takeover performance level (TOPL), is proposed, and a model based on an improved EWM-TOPSIS method is constructed to evaluate TOPL. The model determines the objective weight of each index using the entropy weight method (EWM), and then codes and maps each index based on the positive and negative ideal solutions in the technique for order preference by similarity to ideal solution (TOPSIS) model, thereby constructing the TOPL evaluation model. To verify the effectiveness of the model, 46 drivers participated in a human-machine co-driving takeover experiment, from which multidimensional evaluation indicators representing the safety, comfort, and smoothness of driver takeover performance are extracted. The study examines non-driving-related tasks by drivers during takeover and the impacts of the lead time for takeover requests on the level of driver takeover performance. Furthermore, the study analyzed the significant impacts of driver age and standard non-driving-related task performance on TOPL. The results show that both driver age and non-driving-related task performance scores significantly impact TOPL. Additionally, significant differences in driver mileage are observed between the mileage ranges of 50 000 to 100 000 km, 0 to 50 000 km, and 100 000 to 1 000 000 km. A significant negative correlation exists among the maximum yaw rate, maximum lateral acceleration, maximum lateral velocity, throttle depth standard deviation, and TOPL, whereas the time to reach the takeover boundary is significantly positively correlated with TOPL. In relation to varying takeover time budgets and the performance in completing non-driving tasks, TOPL exhibited the minimum takeover time budget of 4 s, and its takeover performance level is observed to be lower under conditions of emergency takeover. Additionally, when the driver's score in non-driving task completion fell below 60, TOPL recorded the highest values, and the TOPL decreases as the score increases.

     

  • loading
  • [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)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(4)

    Article Metrics

    Article views (70) PDF downloads(13) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return