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Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge and has components for obstacle detection using 3D point clouds, traffic signs detection and classification which employs Convolutional Neural Networks (CNN) and depth information, risk assessment with collision detection using short-term motion.

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Shashank worked on optical and scene flow integration in the CARLA simulator. Supervised by Mathias Gehrig and Javier Hidalgo. ... Simone worked on learning vision-based RL policies for dynamic obstacle avoidance. Supervised by Antonio Loquercio and Yunlong Song. ... By carefully implementing and profiling feature detection, tracking, and non.

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