Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map
This addresses the problem of reducing data labeling needs while maintaining interpretability for autonomous driving systems, representing an incremental improvement over existing methods.
The paper tackles the scalability and interpretability issues in autonomous driving by introducing a self-supervised architecture for multi-step prediction of road dynamics and cost maps, resulting in fewer collisions and road violations compared to baselines in real-world experiments.
While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In contrast, while end-to-end architectures do not require labeled data and are potentially more scalable, interpretability is sacrificed. We introduce a novel architecture that is trained in a fully self-supervised fashion for simultaneous multi-step prediction of space-time cost map and road dynamics. Our solution replaces the manually designed cost function for motion planning with a learned high dimensional cost map that is naturally interpretable and allows diverse contextual information to be integrated without manual data labeling. Experiments on real world driving data show that our solution leads to lower number of collisions and road violations in long planning horizons in comparison to baselines, demonstrating the feasibility of fully self-supervised prediction without sacrificing either scalability or interpretability.