The efficiency and execution time of a road scene evaluation subsystem directly influences a self-driving car's control effectiveness. This article presents a novel approach to fuse data from various sensors (camera, LIDAR, radar, IMU) for pedestrian and vehicle tracking. Our approach, thanks to modern methods of image processing and power of GPU for LIDAR processing, achieves 25Hz update frequency for tracking. The system has been tested on the DiDi-Udacity Self-Driving Challenge datasets.

Car racing control is often assumed to take place in isolation, although the greatest strategic challenges arise in the presence of other cars. Our solution uses a combined nonlinear system model which includes the four-wheeled vehicle dynamics model and point-mass model of its opponent car. This proposed combined model allows the optimal control scheme to automatically find an overtaking strategy. The FORCES code generation tool to handle the consequent computational complexity of the Nonlinear Model Predictive Controller (NMPC). We demonstrate the practicality of the suggested method by performing real-time control in a racing simulation software.