In this paper, we propose an accurate and robust perception module for Autonomous Vehicles (AVs) for drivable space extraction. Perception is crucial in autonomous driving, where many deep learning-based methods, while accurate on benchmark datasets, fail to generalize effectively, especially in diverse and unpredictable environments. Our work introduces a robust easy-to-generalize perception module that leverages LiDAR, camera, and HD map data fusion to deliver a safe and reliable drivable space in all weather conditions. We present an adaptive ground removal and curb detection method integrated with HD map data for enhanced obstacle detection reliability. Additionally, we propose an adaptive DBSCAN clustering algorithm optimized for precipitation noise, and a cost-effective LiDAR-camera frustum association that is resilient to calibration discrepancies. Our comprehensive drivable space representation incorporates all perception data, ensuring compatibility with vehicle dimensions and road regulations. This approach not only improves generalization and efficiency, but also significantly enhances safety in autonomous vehicle operations. Our approach is tested on a real dataset and its reliability is verified during the daily (including harsh snowy weather) operation of our autonomous shuttle, WATonoBus

After recognizing that a collision of some kind is inevitable, the system works by analyzing all available options and choosing the course of action with the least serious outcome. "What can we do in order to minimize the consequences?" said Amir Khajepour, a professor of mechanical and mechatronics engineering at the University of Waterloo. The first rule for the autonomous vehicle (AV) crash-mitigation technology is avoiding collisions with pedestrians. From there, it weighs factors such as relative speeds, angles of collision and differences in mass and vehicle type to determine the best possible manoeuvre, such as braking or steering in one direction or another. "We consider the whole traffic environment perceived by the autonomous vehicle, including all the other vehicles and obstacles around it," said Dongpu Cao, also a mechanical and mechatronics engineering professor at Waterloo.