Détail de l'offre
Offre du 15/11/2022
Comparison methodology of dynamic occupancy grids for autonomous driving
Supervisors: P. Koch, J-B. Horel, C. Laugier
Obstacle detection is a key component of autonomous vehicles. The ability to detect dynamic obstacles, along with estimating their velocity, allows to track them in order to predict future states, hence generate safe commands. A common solution to this problem is to generate dynamic occupancy grids. However, quantitatively evaluating their performance and validating their correct functioning is still a challenge to be solved. This internship will be focused on finding new solutions for the dynamic grids performance evaluation and to use them to compare two existing methods: an in-house algorithm  and a state of the art solution . This work will be done in the Chroma team (1) at Inria Grenoble Rhone-Alpes research center, within the "Plateforme de Recherche et d'Investissement pour la Sûreté et la Sécurité de la Mobilité Autonome" (2) project.
The candidate will be tasked to study and evaluate dynamic occupancy grid based methods, with a particular focus on two existing approaches. To do so, the starting point will be to design suitable metrics, in order to evaluate the perception algorithm at a ”fine” level. Those metrics should be developed with validation  in mind, to be re-used in automated testing. The internship is composed of three main tasks: first, a state of the art of robotic perception metrics, with a focus on occupancy grids, then implementation of metrics toolbox, and finally a thorough analysis of the obtained results.
• Master 2 in robotics, computer science, image processing
• Python, C++, LaTeX, Linux/Ubuntu
• Bonus: OpenCV, ROS1, numpy
4 Additional Information
• The intern will have the opportunity to perform tests on Chroma self-driving vehicle .
• The internship duration is 5 or 6 months, ideally starting from February/March 2023.
• To apply, please send your resume and cover letter to: firstname.lastname@example.org
Type de Contrat
Diplômes et Expérience
Outils et Environnements