Robust Multi-Lane Detection and Tracking in Temporal-Spatial Based on Particle Filtering Tongji University

Format:

Book

Conference/Event

Author/Creator:

Chen, Chen, author.

Contributor:

Bai, Jie

Huang, Libo

Conference Name:

WCX SAE World Congress Experience (2019-04-09 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2019

Summary:

AbstractThe camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image. Then, all the lane marker candidates are clustered into many lanes and as the initialization of the following particle filtering tracking process. We calculate the distance map in the binary slice image by using a modified distance algorithm and sample particles in the distance map. Finally, we can track the lane markers with particle filtering successfully. A range of experiment results indicate that the proposed algorithm can detect and track multiple lanes correctly and robustly. Comparing with other method, it has enough tolerance to variant illumination and occlusion

Notes:

Vendor supplied data

Publisher Number:

2019-01-0885

Access Restriction:

Restricted for use by site license