Visual Road Boundary Detection based Local Path Planner We propose a realtime visual road detection based local path planner for autonomous navigation in unstructured environments. The system consists of two parts: road detection and local path planning. Road boundary model based on convolution neural network is trained for local road detection in image frame. After perspective transformation, the result is transformed to the vehiclexe2x80x99s frame for path planning. The road midline generated from the road boundaries is used to represent the reference local path. Priori information with the global navigation topology map is combined to solve multiroads selection. Finally, data fusion with historical detection results and predictive control of vehicle model is used for path optimization. In addition, obstacle avoidance combined with LIDAR perception is also taken into account to complete an autonomous navigation task in complex scenarios.A large number of experiments with various road conditions shows that the proposed method has a good adaptability and versatility for unstructured scenarios. The algorithm can effectively reduce the dependence on absolute positioning and LIDAR perception. So it is of great importance for autonomous navigation in scenarios without satellite or in unknown scenarios.

Virtual Lane Boundary Generation for HumanCompatible Autonomous Driving: A Tight Coupling between Perception and Planning Existing autonomous vehicle (AV) navigation algorithms treat lane recognition, obstacle avoidance, local path planning, and lane following as separate functional modules which result in driving behavior that is incompatible with human drivers. It is imperative to design humancompatible navigation algorithms to ensure transportation safety. We develop a new tightlycoupled perceptionplanning framework that combines all these functionalities to ensure humancompatibility. Using GPScameralidar sensor fusion, we detect actual lane boundaries (ALBs) and propose availabilityreasonabilityfeasibility (ARF) threefold tests to determine if we should generate virtual lane boundaries (VLBs) or follow ALBs. If needed, VLBs are generated using a dynamically adjustable multiobjective optimization framework that considers obstacle avoidance, trajectory smoothness (to satisfy vehicle kinodynamic constraints), trajectory continuity (to avoid sudden movements), GPS following quality (to execute global plan), and lane following or partial direction following (to meeting human expectation). Consequently, vehicle motion is more human compatible than existing approaches. We have implemented our algorithm and tested under open source data with satisfying results.