Effect of Complex Horizontal Alignment Combination Design on the Trajectory Offset of Autonomous Vehicles Based on PreScan

doi:10.12141/j.issn.1000-565X.240590

Abstract

Abstract:

The penetration of automated vehicles (AVs) is expected to gradually increase in the future. Consequently, adding dedicated lanes for AVs to existing roads has become an effective countermeasure to improve traffic efficiency and driving safety. Although the horizontal and vertical alignment are constrained by human-driven vehicles and difficult to adjust, the width of Dedicated lanes for AVs can be redesigned and optimized. However, there is currently a lack of industry standards and calculation basis for designing such lanes. Vehicle trajectory deviation is a crucial factor in determining lane width. This study focuses on complex horizontal curve combinations that significantly affect driving trajectories. Using the PreScan-Simulink simulation platform, it applied typical AV lateral and longitudinal motion control algorithms and considered three types of complex horizontal curve combinations: oval, convex, and C-shaped. It constructed simulation vehicle models and road scenarios for different vehicle types and analyzed the impact of these complex curve combinations on AV trajectory deviation, ultimately developing trajectory deviation models for various vehicle types. This study shows that, unlike in convex curves where the maximum trajectory deviation occurs at the gentle transition point (HH point), in oval and C-shaped curves, the maximum deviation occurs at the first transition curve point (HY1 point). The design speed is significantly correlated with the trajectory offsets of AV on each horizontal alignment combination design: the offsets of the feature points with the largest offsets on each design are about 9~16 cm for AV at 60~130 km/h; the magnitude of the trajectory offsets varies greatly with the change in design speed, and the offsets of the feature points with the largest offsets on each horizontal alignment combination design are about 13~23 cm for AV at 140~150 km/h. Finally, a polynomial regression model was established to describe the relationship between design speed and trajectory deviation. The R² of the model is greater than 0.95, so the model fit meets the prediction requirements. The research method and research results of this thesis can provide a reference basis for the calculation of dedicated lane width.

Key words: road engineering, autonomous vehicle, dedicated lanes for autonomous vehicle, simulation experiment, trajectory offset

CLC Number:

U412

WANG Xiaofei, HUANG Shiqi, YAO Jiangbei, ZENG Qiang. Effect of Complex Horizontal Alignment Combination Design on the Trajectory Offset of Autonomous Vehicles Based on PreScan[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(7): 104-115.

Figures/Tables 13

Fig.1

Fig.2

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Fig.4

Table 1

Table of experimental designs for oval curve"

车型	设计标准	设计速度/（km·h^-1）	小圆曲线半径 $R 1$ /m	小圆与大圆曲线半径比值 $R 1 / R 2$	圆曲线长度/m	缓和曲线长度/m
小客车	L₁	60	200	0.5，0.65，0.8	100	75
	L₂	80	400	0.5，0.65，0.8	130	100
	L₃	100	700	0.5，0.65，0.8	170	130
	L₄	120	1 000	0.5，0.65，0.8	200	150
	L₅	130	1 150	0.5，0.65，0.8	220	165
	L₆	140	1 300	0.5，0.65，0.8	235	180
	L₇	150	1 500	0.5，0.65，0.8	250	190
大货车	L₁	60	200	0.5，0.65，0.8	100	75
	L₂	80	400	0.5，0.65，0.8	130	100
	L₃	100	700	0.5，0.65，0.8	170	130

Table 1

Table 2

Table 3

Table of experimental designs for C-shape curve"

车型	设计标准	设计速度/（km·h^-1）	前圆曲线半径 $R 1$ /m	后圆、前圆曲线半径比值 $R 2 / R 1$	圆曲线长度/m	缓和曲线长度/m
小客车	L₁	60	200	1.0，1.5，2.0	100	75
	L₂	80	400	1.0，1.5，2.0	130	100
	L₃	100	700	1.0，1.5，2.0	170	130
	L₄	120	1 000	1.0，1.5，2.0	200	150
	L₅	130	1 150	1.0，1.5，2.0	220	165
	L₆	140	1 300	1.0，1.5，2.0	235	180
	L₇	150	1 500	1.0，1.5，2.0	250	190
大货车	L₁	60	200	1.0，1.5，2.0	100	75
	L₂	80	400	1.0，1.5，2.0	130	100
	L₃	100	700	1.0，1.5，2.0	170	130

Table 3

Fig.5

Fig.6

Fig.7

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Fig.9

Fig.10

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