Modelling of LIDAR sensor disturbances by solid airborne particles

This paper aims to introduce a method for simulating with a real time performance the automotive LIDAR disturbance by dust clouds caused by natural phenomena, mechanical or man-made processes like a traveling vehicle. In this study, we are interested to study the interaction of an automotive LIDAR sensor with a dust cloud composed of solid particles. The main objective of this study is to provide a simulation model to industry and research laboratories that help to study LIDAR performance in a dust-sand environment with the capability to reproduce the encountered problems in degraded conditions and the ability to parameterize the degradation model. Based on industrial projects with a passenger’s vehicles and truck manufacturers, we present LIDAR sensor and functionalities to perceive objects in a scene (pedestrian, car, truck, ...) in clear or extreme weather conditions. Simulated and experimental data are compared and analyzed in this article. The features presented are evaluated according to their quality for object detection. This study can be applied to sensors post-processing algorithms (object recognition, tracking, data fusion...) and even to the design of cleaning systems.

[1]  Roland Siegwart,et al.  Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning , 2019, FSR.

[2]  Philip J. Sallis,et al.  Weather Detection in Vehicles by Means of Camera and LIDAR Systems , 2014, 2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks.

[3]  Fernando Garcia,et al.  A Review of Sensor Technologies for Perception in Automated Driving , 2019, IEEE Intelligent Transportation Systems Magazine.

[4]  José Eugenio Naranjo,et al.  Environment perception based on LIDAR sensors for real road applications , 2011, Robotica.

[5]  Xavier Savatier,et al.  Vehicle-Hardware-In-The-Loop system for ADAS prototyping and validation , 2014, 2014 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV).

[6]  Nicky Guenther,et al.  When the Dust Settles: The Four Behaviors of LiDAR in the Presence of Fine Airborne Particulates , 2017, J. Field Robotics.

[7]  Wilhelm Stork,et al.  Weather Influence and Classification with Automotive Lidar Sensors , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[8]  Laurent Hespel,et al.  Impact of dust particle shape and water coating on multiwavelength lidar signals , 2014 .

[9]  Jia Chen,et al.  Characterization and simulation of the effect of road dirt on the performance of a laser scanner , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[10]  Mokrane Hadj-Bachir,et al.  LIDAR sensor simulation in adverse weather condition for driving assistance development , 2019 .

[11]  Homayoun Najjaran,et al.  Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review , 2020, Sensors.

[12]  Ralph Helmar Rasshofer,et al.  Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions , 2005 .

[13]  Dominique Gruyer,et al.  Automotive LIDAR objects detection and classification algorithm using the belief theory , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).