PT  - JOURNAL ARTICLE
AU  - Feraco, Stefano
AU  - Bonfitto, Angelo
AU  - Amati, Nicola
AU  - Tonoli, Andrea
TI  - Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments
DP  - 2022 Jan 1
TA  - Communications - Scientific Letters of the University of Zilina
PG  - C1--C17
VI  - 24
IP  - 1
AID - 10.26552/com.C.2022.1.C1-C17
IS  - 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.