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Path Generation Algorithm Based on Crash Point Prediction for Lane Changing of Autonomous Vehicles

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Abstract

To reduce the calculation time needed to determine the optimal path, the form of the road and the path of an autonomous vehicle were linearized; additionally, among multiple obstacles, only those that were potentially dangerous were chosen. By considering the movement of moving obstacles, the cost was calculated. The calculation time was shortened by reducing the number of design variables of the optimal path, when changing lanes to avoid obstacles, to two. Limiting conditions, such as the lateral and longitudinal acceleration, were excluded from the cost calculation by restricting the search region of the design variable. The final result was calculated using a relatively free search of the golden-section search regarding the initial value setting. For the golden-section search, the number of final design variables was reduced to one; this was done by optimizing the search direction. The search direction was determined based on the final position of the vehicles and the calculated optimal points. By including a collision avoidance algorithm and moving in a short period of time, the calculated optimal path prevented accidents due to path errors caused by simplification. The path could be found easily, even for complex road shapes and with multiple vehicles nearby.

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Abbreviations

α :

hading error of the autonomous vehicle, rad

ε :

lateral offset error of the autonomous vehicle, m

θ :

heading angle for the final target path, rad

a :

cubic coefficient of the autonomous vehicle path, 1

b :

quadratic coefficient of the autonomous vehicle path, 1

c :

linear coefficient of the autonomous vehicle path, 1

d :

constant value of the autonomous vehicle path, 1

t i :

interval time for collision detection, s

t f :

fillet time, s

t lat :

look ahead time, s

t p1 :

lower bound of tp2, s

t p2 :

solution result of the target time, s

t p3 :

upper bound of tp2, s

x ″ :

linearly mapped x-coordinate, m

x 0 :

x-coordinate of the autonomous vehicle, m

\({\hat x_0}\) :

estimated x-coordinate of the autonomous vehicle, m

x i :

x-coordinate of the obstacle, m

\({\hat x_{\rm{i}}}\) :

estimated x-coordinate of the obstacle, m

x p1 :

lower bound of xp2, m

x p2 :

solution result of the target longitudinal distance, m

x p3 :

upper bound of xp2, m

x target :

target point x, m

x t,i :

expanded target point

y ″ :

linearly mapped y-coordinate, m

y 0 :

y-coordinate of the autonomous vehicle, m

ŷ 0 :

estimated y-coordinate of the autonomous vehicle, m

y i :

y-coordinate of the obstacle, m

ŷ i :

estimated y-coordinate of the obstacle, m

y target :

target point y, m

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Acknowledgement

This work was supported in part by Technology Innovation Program No. 10062828, ‘Development of Human Machine Interface for the Driving Control Authority Transition of Autonomous Vehicles using Steering Torque Control’ funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). This research was also supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (iITP-2018-0-01426) supervised by the IITP (Institute for Information & communications Technology Promotion).

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  1. Department of Mechanical Engineering, Sungkyunkwan University, Gyeonggi, 16419, Korea

    Chanho Park, Nak-Tak Jeong, Dongyeon Yu & Sung-Ho Hwang

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  1. Chanho Park
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  2. Nak-Tak Jeong
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Correspondence to Sung-Ho Hwang.

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Park, C., Jeong, NT., Yu, D. et al. Path Generation Algorithm Based on Crash Point Prediction for Lane Changing of Autonomous Vehicles. Int.J Automot. Technol. 20, 507–519 (2019). https://doi.org/10.1007/s12239-019-0048-1

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  • DOI: https://doi.org/10.1007/s12239-019-0048-1

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