High-Definition Map Based Motion Planning, and Control for Urban Autonomous Driving Seoul National University

Format:

Book

Conference/Event

Author/Creator:

Yoon, Youngmin, author.

Contributor:

Chae, Heungseok

Yi, Kyongsu

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

This paper presents motion planning and control algorithm for urban automated driving using high-definition(HD) map. Many automakers have developed and commercialized advanced driver assistance system(ADAS) based on vision-only lane extraction in motorway environments. Compared to the motorway environments where the lane is continuous and clearly visible, however, in urban roads, degradation of the lane quality such as lane occlusion and lane loss occurs frequently. This leads to the poor quality of the local guide path for the autonomous vehicles with vision-only lane extraction. Global HD map is used to provide the lane information continuously instead of vision-only lane extraction. With the existence of global position of host vehicle and the HD map, the proposed sequential algorithm performs the lane keeping and lane changing decision and control with safety margin in multi-vehicle situation. The perceived surrounding vehicles are classified according to their road belonging and their future states are estimated based on probabilistic prediction. The motion planning algorithm determines the lane keeping or lane changing decision and corresponding reference states for control algorithm. The control algorithm determines the steering angle and longitudinal acceleration for the desired behavior with model predictive control(MPC) approach [1]. The proposed algorithm has been validated via vehicle tests. The results show that the proposed motion planning and control method guarantees adequate safety distance with surrounding vehicles and appropriate ride quality in urban multi-vehicle traffic scenarios

Notes:

Vendor supplied data

Publisher Number:

2021-01-0098

Access Restriction:

Restricted for use by site license