Artificial Intelligence
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Integrated Computer-Aided Engineering
Prediction-based geometric feature extraction for 2D laser scanner
Robotics and Autonomous Systems
IEEE Spectrum
Automated On-Ramp Merging System for Congested Traffic Situations
IEEE Transactions on Intelligent Transportation Systems
Improved ultra wideband-based tracking of twin-receiver automated guided vehicles
Integrated Computer-Aided Engineering - Anniversary Volume: Celebrating 20 Years of Excellence
Vehicle-to-grid communication system for electric vehicle charging
Integrated Computer-Aided Engineering - Anniversary Volume: Celebrating 20 Years of Excellence
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Light Detection and Ranging LIDAR sensors are commonly used in perception for autonomous vehicles because of their high accuracy, speed, and range. These characteristics make the sensor suitable for integration into the perception layer of controllers which have the capacity to avoid collisions with unpredicted obstacles. The objective of this work was to design a robust and efficient algorithm to acquire useful knowledge from LIDAR scans, and to test its performance in real road situations. The method is based on the construction of an Occupancy Grid based on the Transferable Belief Model, which has proved promising in other studies. Two clear advantages of this method are the reduction of the problem complexity in the phases of segmentation, occlusion detection and tracking, and its ease of integration with other sensors to allow the overall system to evolve towards reliable and complete perception.