Extended Kalman Filter Based Path-Planning Algorithm for Autonomous Vehicles with I2V Communication

• Published in: IFAC-PapersOnLine 2016, Vol.49 (1), p.652-657
• Main Authors: Perumal, D Ganesha, Subathra, B, Saravanakumar, G, Srinivasan, Seshadhri
• Format: Article
• Language: English
• Subjects: Autonomous Vehicle Path-Planning (AVPP); behaviour based controller; Extended Kalman Filter (EKF); Infrastructure-to-vehicle (I2V)
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2016.03.130

Path-planning algorithms that can circumvent moving obstacles are required for realizing reliable autonomous vehicles. This investigation presents a path-planning algorithm for autonomous vehicle (AV) that uses infrastructure-to-vehicle (I2V) communication, Extended Kalman Filter (EKF) and behaviour based hybrid controllers for circumventing moving obstacles. The I2V communication provides the measurements required for estimating the current vehicle position. Vehicle mounted sensors are used to ascertain the current obstacle position and the dynamical equations are used to predict the future obstacle position. The EKF then estimates the future position of the vehicle relative to the obstacles and uses a hybrid controller that switches among the possible control actions. Our results show that the EKF based path-planning algorithm is able to circumvent moving obstacles even with non-linear dynamics.