Analysis of takeover time of automated driving based on survival analysis

doi:10.16265/j.cnki.issn1003-3033.2023.08.1474

Abstract

Abstract:

In order to gain a comprehensive understanding of the impacts of factors including non-driving related tasks, takeover request lead time, type of takeover events, and traffic volume on takeover time during the automated driving takeover process, an orthogonal experimental design was applied to develop the takeover scenarios. Drivers were recruited to participate in the takeover experiments based on a driving simulator, with driver behavior and vehicle trajectory data collected. Factors' impacts on drivers' takeover time were analyzed, and a random-effect survival duration model was developed to predict takeover time. Results reveal that compared with no secondary tasks, performing the non-driving related tasks increased takeover time significantly. When the takeover request lead time was 4 s, takeover time was shorter than that with 5 and 6 s. Takeover time in the accident car and cut-in events was longer than that for the work zone event. Moreover, larger traffic volume led to shorter takeover time. Compared with the fixed-effects survival analysis model, the random-effects survival analysis model accounted for the unobserved heterogeneity in the data and provided good goodness-of-fitness and prediction accuracy.

Key words: automated driving, takeover time, driving simulation, survival analysis, random effect

WANG Changshuai, XU Chengcheng, SHAO Yongcheng, TONG Hao, PENG Chang. Analysis of takeover time of automated driving based on survival analysis[J]. China Safety Science Journal, 2023, 33(8): 142-148.

Figures/Tables 9

Fig.1

Fig.2

Tab.1

Fig.3

Fig.4

Tab.2

Fig.5

Tab.3

Estimated results of the model

变量	参数值	标准差	P值
新手驾驶人	-0.025	0.036	0.499
女性	0.038	0.039	0.333
看视频	0.172	0.018	<0.001
玩游戏	0.193	0.018	<0.001
事故车辆	0.070	0.017	<0.001
换道并减速	0.089	0.017	<0.001
接管请求时间	0.023	0.009	0.009
交通流量	-0.089	0.012	<0.001
截距	0.316	0.057	<0.001
σ	0.110	0.011	<0.001
随机效应	0.010	0.008	<0.001
韦布尔分布参数 $μ$ 的值	2.111	0.045	<0.001
LL(β)	-2.553	χ²(9)	200.64

Tab.3

Fig.6

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接管场景	任务类型	接管请求时间	交通流量	接管事件
1	看视频	4	500	事故车辆
2	看视频	5	1 000	施工区
3	看视频	6	2 000	换道并减速
4	玩游戏	4	1 000	换道并减速
5	玩游戏	5	2 000	事故车辆
6	玩游戏	6	500	施工区
7	无次任务	4	2 000	施工区
8	无次任务	5	500	换道并减速
9	无次任务	6	1 000	事故车辆

变量名称	类别	接管时间/s
驾驶人类型	经验驾驶人	1.59±0.347
驾驶人类型	新手驾驶人	1.58±0.302
性别	女性	1.62±0.336
性别	男性	1.57±0.315
非驾驶相关任务	看视频	1.67±0.245
	玩游戏	1.72±0.255
	无次任务	1.37±0.344
接管请求时间/s	4	1.51±0.396
	5	1.64±0.272
	6	1.61±0.269
接管事件	事故车辆	1.61±0.277
	施工区	1.50±0.391
	换道并减速	1.65±0.268
交通流量/ (辆·h^-1)	500	1.65±0.285
	1 000	1.63±0.301
	2 000	1.47±0.348