Autonomous Emergency Breaking (AEB) Evaluation For Indian Traffic Scenarios using GPS and LiDAR Data

Autonomous emergency braking (AEB) and forward-collision warning (FCW) is a cutting-edge active safety technology that assists drivers in avoiding or minimizing crashes with global cars and other vulnerable road users (VRU) for SAE autonomy Level 3 and 4 categories. The Indian traffic scenario data is recorded for Hyderabad city using Lidar and GPS sensors. This dataset is available at IIIT Hyderabad. This real-world traffic scenario is converted into a virtual closed-loop scenario for evaluating AEB and FCW for different VRUs in this present study. The synthetic Radar and camera data are generated by the radar detection generator and vision detection generator blocks in the driving scenario simulator. The results of the virtual scenario developed using sensor perception reveals the ego car velocity ego car acceleration when the lead vehicle's time-to-collision (TTC) is less than ƮFCW . The simulation results are observed for a total of 9 seconds. The control algorithms for AEB and FCW prevent accidents with rear-end collisions of global vehicles, and accurate world traffic scenario data assessed its performance.