TY - GEN
T1 - Deep Multi-Sensor Lane Detection
AU - Bai, Min
AU - Mattyus, Gellert
AU - Homayounfar, Namdar
AU - Wang, Shenlong
AU - Lakshmikanth, Shrinidhi Kowshika
AU - Urtasun, Raquel
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.
AB - Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.
UR - http://www.scopus.com/inward/record.url?scp=85063000973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063000973&partnerID=8YFLogxK
U2 - 10.1109/IROS.2018.8594388
DO - 10.1109/IROS.2018.8594388
M3 - Conference contribution
AN - SCOPUS:85063000973
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3102
EP - 3109
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Y2 - 1 October 2018 through 5 October 2018
ER -