Decision Making and Trajectory Planning of Intelligent Vehicle' s Lane-changing Behavior on Highways under Multi-objective Constrains Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Nie, Linzhen, author.

Contributor:

Huang, Haoran

Yin, Zhishuai

Conference Name:

WCX SAE World Congress Experience (2020-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

Discretionary lane-changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposes to optimize the decision making and trajectory planning process so that intelligent vehicles make lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver' s expectation. The mechanism of how various factors contribute to the driver's intention to change lanes is studied by carrying out a series of driving simulation experiments, and a Lane-changing Intention Generation (LIG) model based on Convolutional Neural Network (CNN) is proposed. The inputs of the CNN are data fragments of several influence factors including the relative speed and the distance between the subject vehicle and the preceding vehicles in current lane and both sides of the lane, and the type of the preceding vehicles in current lane and both sides of the lane, the average speed of the left and right traffic flow, the in a certain period of time, which is determined via examining subjects' visual behaviors of rearview mirrors. By combining the LIG model with a feasibility judgement model in a multi-lane scenario, a lane-changing behavior decision making model satisfying driving safety and drivers' expectation is proposed. The model is trained with a reconstructed Next Generation Simulation (NGSIM) trajectory dataset. The road surface condition and vehicle' s physical limitation is taken into full consideration, and the jerk optimization method is used to smooth the planned lane-change path to guarantee driving safety and comfort. The proposed lane-changing model is verified against a NGSIM test dataset and the results show that the model resemble the lane-changing behavior of human drivers in real-world

Notes:

Vendor supplied data

Publisher Number:

2020-01-0124

Access Restriction:

Restricted for use by site license