PT Journal
AU Feraco, S
   Bonfitto, A
   Amati, N
   Tonoli, A
TI Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments
SO Communications - Scientific Letters of the University of Zilina
PY 2022
BP C1
EP C17
VL 24
IS 1
DI 10.26552/com.C.2022.1.C1-C17
WP https://komunikacie.uniza.sk/artkey/csl-202201-0009.php
DE perception; autonomous driving; obstacle detection; point-cloud segmentation; single shot detector; LiDAR (Light Detection and Ranging)
SN 13354205
AB This paper presents a redundant multi-object detection method for autonomous driving, exploiting a combination of Light Detection and Ranging (LiDAR) and stereocamera sensors to detect different obstacles. These sensors are used for distinct perception pipelines considering a custom hardware/software architecture deployed on a self-driving electric racing vehicle. Consequently, the creation of a local map with respect to the vehicle position enables development of further local trajectory planning algorithms. The LiDAR-based algorithm exploits segmentation of point clouds for the ground filtering and obstacle detection. The stereocamera-based perception pipeline is based on a Single Shot Detector using a deep learning neural network. The presented algorithm is experimentally validated on the instrumented vehicle during different driving maneuvers.
ER