Ricardo Omar Chavez Garcia - Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments

Organized by: 

Ricardo Omar Chavez Garcia


Ricardo Omar Chavez Garcia

Thèse préparée au sein de l'équipe AMA (LIG) et encadrée par Olivier AYCARD
Lieu de soutenance Salle Alan M. Turing de Centre Equation 4
Jury :

- Michel DEVY, LAAS-CNRS Toulouse, Rapporteur

- François CHARPILLET, INRIA Nancy, Rapporteur

- Michelle ROMBAUT, Gipsa-lab, Examinatrice

- Yassine RUICHEK, Université de Technologie de Belfort-Montbéliard, Examinateur

- Olivier AYCARD, Université de Grenoble 1, Directeur de thèse 

Advanced driver assistance systems (ADAS) help drivers to perform complex driving tasks and to avoid or mitigate dangerous situations. The vehicle senses the external world using sensors and then builds and updates an internal model of the environment configuration. Vehicle perception consists of establishing the spatial and temporal relationships between the vehicle and the static and moving obstacles in the environment. Vehicle perception is composed of two main tasks: simultaneous localization and mapping (SLAM) deals with modelling static parts; and detection and tracking moving objects (DATMO) is responsible for modelling moving parts of the environment. The accurate detection and classification of moving objects is a critical aspect of a moving object tracking system. Therefore, many sensors are part of a common intelligent vehicle system. Multiple sensor fusion approaches combine information from different views of the environment to obtain a more accurate model. This is achieved by combining redundant and complementary measurements of the environment. Fusion can be performed at different levels inside the perception task. 
Classification of moving objects is needed to determine the possible behaviour of the objects surrounding the vehicle, and it is usually performed at tracking level. Knowledge about the class of moving objects at detection level can help to improve their tracking, reason about their behaviour, and decide what to do according to their nature. Most of the current perception solutions consider classification information only as aggregate information for the final perception output. Also, the management of incomplete information is an important issue in these systems. Incomplete information can be originated from sensor-related reasons, such as calibration issues and hardware malfunctions; or from scene perturbations, like occlusions, weather issues and object shifting. 
The main contributions in this dissertation focus on the DATMO stage of the perception problem. Precisely, we believe that including the object's class as a key element of the object's representation and managing the uncertainty from multiple sensors detections, we can improve the results of the perception task, i.e., a more reliable list of moving objects of interest represented by their dynamic state and appearance information. Therefore, we address the problems of sensor data association, and sensor fusion for object detection, classification, and tracking at different levels within the DATMO stage. Although we focus on a set of three main sensors: radar, lidar, and camera, we propose a modifiable architecture to include other type or number of sensors. We propose, implement, and compare two different perception architectures to solve the DATMO problem according to the level where object association, fusion, and classification information is included and performed. Our data fusion approaches are based on the Evidential framework, which is used to manage and include the uncertainty from sensor detections and object classifications. We integrate the proposed fusion approaches as a part of a real-time vehicle application inside a vehicle demonstrator from the interactIVe European project. We evaluate our Evidential fusion approaches using real data from different driving scenarios and focusing on the detection, classification and tracking of different moving objects: pedestrian, bike, car and truck.