Nowadays, navigating autonomous vehicle (AV)/driverless vehicle is becoming an important requirement in different application areas. AVs are the vehicles that are capable of sensing environment around them for moving on the road without any human intervention. The AV senses the environment with the help of sensors configured in the AV like Laser, Light Detection and Ranging (LIDAR), Global Positioning System (GPS), computer vision and the like. A control system associated with the AV may receive inputs from the sensors, based on which the control system may identify appropriate navigation path, obstacles in the dynamically changing environment and the like.
Generally, identifying the navigation path for the AV may include a combination of three basic abilities such as localization, path planning and vehicle control. Localization determines ability of the AV to calculate its current position and orientation within a global reference frame. Path planning determines path and sequence of command velocity to reach a desired destination from current position of the AV. The planned path may be followed by the AV using a feedback controller system which includes dynamic obstacle avoidance as well as global path pre-planning and/or re-planning.
Existing techniques for navigating the AV include classical planning, case based planning, coordinated robot planning and the like. However, for path planning, existing techniques only focus on either collision avoidance or shortest path between a source point and a destination point as a primary criterion. However, currently, there exists no mechanism to check whether the detected shortest path for navigating the driverless vehicle is a safe path or not. Further, the existing techniques use numerous sensors to navigate the AV that makes the overall system complex.