Lane‐changing trajectory planning method for automated vehicles under various road line‐types. Issue 1 (2nd March 2020)

• Record Type: Journal Article
• Authors: Wang, Ying; Wei, Chong; Liu, Erjian; Li, Shurong
• Is Part Of: IET smart cities. Volume 2:Issue 1(2020)
• Page Start: 14
• Page End: 23
• Publication Date: 2020-03-02
• Subjects: regression analysis -- road vehicles -- road safety -- optimisation -- road traffic control -- polynomials -- trajectory control -- collision avoidance -- mobile robots; lane‐changing process -- target lane -- lag vehicle -- ego vehicle -- car‐following manoeuvre -- lane‐changing manoeuvre -- road polynomial functions -- nonlinear optimisation model -- polynomial regression model -- road line‐types -- automated vehicle -- lane‐changing trajectory planning method; C1110 Algebra -- C1140Z Other topics in statistics -- C1180 Optimisation techniques -- C3120C Spatial variables control -- C3360B Road‐traffic system control -- C3390C Mobile robots; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/iet-smc.2019.0079
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract : This study proposes a lane‐changing trajectory planning method for automated vehicles under various road line‐types. The method uses the polynomial regression model to describe the road line‐types, and then a non‐linear optimisation model is constructed to generate the lane‐changing trajectory based on the road polynomial functions. The process of connecting the lane‐changing manoeuvre with the car‐following manoeuvre is discussed in this study, which ensures the ride comfort of the ego vehicle after the lane‐changing manoeuvre. Moreover, considering that the lag vehicle on the target lane may be affected by the lane‐changing manoeuvre, the situation that the lag vehicle maintains the car‐following manoeuvre with the ego vehicle is taken into account in the authors' model. Another small innovation is that they have designed a simple and effective method to find the suitable initial guess for the proposed non‐linear optimisation model. The simulation results show that the lane‐changing trajectory generated by the proposed model is smooth and continuous, and the automated vehicle can avoid potential collisions efficiently during the lane‐changing process. In emergent conditions, the proposed model can also plan the corrected trajectory to ensure safety.