Efficient and Robust
LiDAR-Based
End-to-End Navigation
Paper πππ
LiDAR-Based
End-to-End Navigation
Paper πππ
Paper: arxiv.org/pdf/2105.09932
Credit: Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela Rus
Credit: Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, Daniela Rus
Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input.
While LiDAR sensors provide reliably accurate information, existing end-to-end driving solutions are mainly based on cameras since processing 3D data requires a large memory footprint and computation cost.
On the other hand, increasing the robustness of these systems is also critical; however, even estimating the model's uncertainty is very challenging due to the cost of sampling-based methods.
In this paper, they present an efficient and robust LiDAR-based end-to-end navigation framework.
They first introduce Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design.
They then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass and then fuses the control predictions intelligently.
They evaluate their system on a full-scale vehicle and demonstrate lane-stable as well as navigation capabilities.
In the presence of out-of-distribution events (e.g., sensor failures), their system significantly improves robustness and reduces the number of takeovers in the real world.
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