Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles
This addresses safety and reliability issues for autonomous vehicles in real-world long-tail data distributions, but it is incremental as it builds on existing uncertainty-aware planning approaches.
The paper tackles the problem of trajectory planning failures in self-driving vehicles due to prediction model uncertainty in long-tail driving cases with sparse data, proposing a method that improves safety by considering worst-case scenarios without overly conservative results when data is sufficient.
A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles. Recently, deep learning technology has been widely adopted to design prediction models due to their impressive performance. However, such models may fail in the "long-tail" driving cases where the training data is sparse or unavailable, leading to planner failures. To this end, this work proposes a trajectory planner to consider the prediction model uncertainty arising from insufficient data for safer performance. Firstly, an ensemble network structure estimates the prediction model's uncertainty due to insufficient training data. Then a trajectory planner is designed to consider the worst-case arising from prediction uncertainty. The results show that the proposed method can improve the safety of trajectory planning under the prediction uncertainty caused by insufficient data. At the same time, with sufficient data, the framework will not lead to overly conservative results. This technology helps to improve the safety and reliability of autonomous vehicles under the long-tail data distribution of the real world.