IIIT Hyderabad Publications |
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Motion Planning Framework for Autonomous Vehicle : A Time Scaled Collision Cone approachAuthor: A. V. S. Sai Bhargav Kumar Date: 2019-08-19 Report no: IIIT/TH/2019/108 Advisor:Madhava Krishna AbstractThe research in the field of autonomous driving is making significant progress a lot of impor-tance is being placed on developing reliable motion planning frameworks that scale up to complex scenarios like urban traffic, highway driving, etc. Planning frameworks that cater to the current autonomous navigation stacks must have the capability to generate maneuvers that are real-time feasible and robust enough to accommodate the unpredictable behaviour of traffic participants. A typical reactive navigation scenario places a lot of emphasis on the computational efficiency of such planners, as the sensing and planning cycles should be in perfect synchronization. The current motion planning modules for autonomous vehicles consider large safety margins and use predefined paths for performing complex maneuvers like merging, overtaking, etc. These considerations generally result in maneuvers that are very conservative in nature and are far from being optimal. In a real urban like scenario, autonomous vehicles adopting such planning modules may cause situations like traffic disruption and unwanted deadlocks. In the proposed thesis we present a motion planning framework for autonomous vehicles to handle complex maneuvers like merging in dense traffic. At the primary level, we present an end to end trajectory optimization scheme that uses state of the art techniques to generate trajectories that are not only kinematically feasible but also very smooth to the extent of being jerk minimal. One of the other highlights here is an efficient Sequential Convex Optimization routine that has the capability to handle differential constraints which are very crucial for dynamic obstacle avoidance. To sum up, the proposed optimization scheme in this level maintains a fine balance between the spatial and temporal modifications that are carried out on a trajectory to avoid collisions consistently from start to goal. At the secondary level, we developed a risk based lane selection mechanism which forms the Lane selection layer of the proposed framework. This layer computes the likelihood of collision along each trajectory by deriving the probability distribution of the first order Collision cone constraint in closed form. One of the notable highlights here is the closed form nature of this probability distribution, which makes the computation of Collision likelihood very efficient. The trajectory that has least collision risk is then selected for performing the final maneuver. We show that the presented lane selection mechanism significantly reduces the temporal deviation causing the least disruption to the traffic flow.To demonstrate the efficacy of the presented framework we have evaluated it in several highway scenarios in simulations. Also, the framework is deployed on a real autonomous car without the lane selection layer and, its performance is evaluated on both static and dynamic environments. Full thesis: pdf Centre for Robotics |
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