Dempster Shafer Gridbased Hybrid Fusion of Virtual Lanes for Autonomous Driving Lane estimation plays a central role in the context of selfdriving vehicles, requiring clear road markings and a high quality infrastructure. To meet the safety requirements and manage difficult use cases (i.e. poor quality or absence of road marking, traffic jams, severe weather conditions, etc.) without depending on cartography and lane markings, we propose a lidarbased Virtual Lanes (VL) generation system to provide a comfortable and safe ride. For this purpose, hybrid fusion based on DempsterShafer Theory (DST) coupled with a particle filter is introduced. Two VL strategies are merged: the first constructs virtual lanes based on independent roadborders detection, while the second uses moving objects trajectories. A novel lane similarity computation is also adopted in order to estimate an effective lane comparison. The performance is reported through extensive experiments with Valeo democar on highway and beltway roads. Experimental results demonstrate the accurate and robust performance of the proposed system.

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.