This work presents the algorithms and system architecture of an automated car aimed at performing a slalom use-case. Besides the sensors integrated into the Panamera car, the system is equipped with additional Differential GPS, mono camera, and 16-layers Lidar. The algorithms run on NVIDIA TX2, they are integrated using ROS middleware and interfaced with the car ECUs via FlexRay. HOG descriptor is used to create a feature vector, and SVM is used for cones classification. Data from the camera, Lidar, and DGPS are used to localize the cones and generate the map. Kalman filter derives an accurate car position and heading from DGPS and the car odometry. The bicycle model is used to formulate a nonlinear optimization problem with quadratic criterion aiming at optimal trajectory planning while respecting the car kinematics. Finally, the trajectory following and lateral & longitudinal controllers are driving the car in the slalom. Substantial work was devoted to the experiments with a real vehicle and the fine-tuning of the system parameters. Validation of the system reveals exciting observations related to the precision, frequency, and sensitivity of the system components.