Real-time testing of AI enabled automatic emergency braking system for ADAS vehicle using 3D point cloud and precise depth information

Abstract

At the forefront of automobile safety technology, Automatic Emergency Braking (AEB) represents a major advancement in collision avoidance systems. The cutting-edge technology provides an additional layer of security at pivotal times, making it a vital part of the changing landscape of vehicle safety. This research introduces an effective system for automatic emergency braking in ADAS-equipped or Autonomous vehicles using a combination of 3d lidar and stereo vision camera for a swift and robust system in the vehicle that is faster than human drivers in cases of unexpected emergencies. Utilizing the power of clustering algorithms on 3d point clouds and state-of-the-art computer vision algorithms on an RGB image mapped to a depth frame from the stereo vision camera, the system as a whole provides a comprehensive system adding to the safety of the vehicle and the passengers. Further, the efficiency of the system is studied based on various parameters. The data from an external inertial measurement unit is also utilized to derive results that support the claims of the study. The system has been developed and implemented on a passenger car that has been modified into an electric vehicle and further tested in real-world traffic conditions in autonomous driving mode. The findings of the study were having exceptionally good precision in split-second decision-making in emergency maneuvers.