Probabilistic Trajectory Prediction

Automated driving requires predictions of where other road users may move and how uncertain those predictions are. At Bosch, I developed a Gaussian regression-based method for trajectory prediction with explicit uncertainty estimates. I evaluated forecasting accuracy and uncertainty calibration against production baselines and translated Python research prototypes into C++ systems with defined reliability requirements. The broader programme included analysis of more than 20,000 hours of driving data and technical leadership of an eleven-person environment-prediction team. The patent application and demonstration document the trajectory-prediction method, connecting probabilistic modeling with the practical constraints of real-time software.