Winning the ICCV 2019 Learning to Drive Challenge
This work addresses the challenge of safe and comfortable autonomous driving by improving trajectory prediction, though it is incremental as it builds on existing methods for a specific competition.
The paper tackled the problem of predicting vehicle trajectories (angle and speed) for autonomous driving by fusing camera sensor inputs with visual map data, achieving the best mean squared error for angle and overall performance to win the ICCV 2019 Learning to Drive challenge.
Autonomous driving has a significant impact on society. Predicting vehicle trajectories, specifically, angle and speed, is important for safe and comfortable driving. This work focuses on fusing inputs from camera sensors and visual map data which lead to significant improvement in performance and plays a key role in winning the challenge. We use pre-trained CNN's for processing image frames, a neural network for fusing the image representation with visual map data, and train a sequence model for time series prediction. We demonstrate the best performing MSE angle and best performance overall, to win the ICCV 2019 Learning to Drive challenge. We make our models and code publicly available.