An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

• Published in: Robotics and autonomous systems 2021-05, Vol.139, p.103662, Article 103662
• Main Authors: Bersani, Mattia, Mentasti, Simone, Dahal, Pragyan, Arrigoni, Stefano, Vignati, Michele, Cheli, Federico, Matteucci, Matteo
• Format: Article
• Language: English
• Subjects: Autonomous driving; Obstacles tracking; Sensor fusion; State estimation
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2020.103662

Understanding of the driving scenario represents a necessary condition for autonomous driving. Within the control routine of an autonomous vehicle, it represents the preliminary step for the motion planning system. Estimation algorithms hence need to handle a considerable number of information coming from multiple sensors, to provide estimates regarding the motion of ego-vehicle and surrounding obstacles. Furthermore, tracking is crucial in obstacles state estimation, because it ensures obstacles recognition during time. This paper presents an integrated algorithm for the estimation of ego-vehicle and obstacles’ positioning and motion along a given road, modeled in curvilinear coordinates. Sensor fusion deals with information coming from two Radars and a Lidar to identify and track obstacles. The algorithm has been validated through experimental tests carried on a prototype of an autonomous vehicle. •The estimation process is a fundamental task for autonomous driving.•Estimates are related to the ego-vehicle and the surrounding obstacles.•The estimation routine handles in proper way the model nonlinearities.•Estimates are provided in the local reference frame of the road.•The algorithm performs sensor-fusion and estimation in real-time.