Learning an Uncertainty-Aware Object Detector for Autonomous Driving

The capability to detect objects is a core part of autonomous driving. Due to sensor noise and incomplete data, perfectly detecting and localizing every object is infeasible. Therefore, it is important for a detector to provide the amount of uncertainty in each prediction. Providing the autonomous system with reliable uncertainties enables the vehicle to react differently based on the level of uncertainty. Previous work has estimated the uncertainty in a detection by predicting a probability distribution over object bounding boxes. In this work, we propose a method to improve the ability to learn the probability distribution by considering the potential noise in the ground-truth labeled data. Our proposed approach improves not only the accuracy of the learned distribution but also the object detection performance.

[1]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[2]  Leonidas J. Guibas,et al.  Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Zoubin Ghahramani,et al.  Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference , 2015, ArXiv.

[4]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[5]  Mark J. F. Gales,et al.  Predictive Uncertainty Estimation via Prior Networks , 2018, NeurIPS.

[6]  Yarin Gal,et al.  Uncertainty in Deep Learning , 2016 .

[7]  Yuning Jiang,et al.  Acquisition of Localization Confidence for Accurate Object Detection , 2018, ECCV.

[8]  Fernando García,et al.  BirdNet: A 3D Object Detection Framework from LiDAR Information , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[9]  Tian Xia,et al.  Vehicle Detection from 3D Lidar Using Fully Convolutional Network , 2016, Robotics: Science and Systems.

[10]  Alex Kendall,et al.  What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.

[11]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[12]  Bin Yang,et al.  Deep Continuous Fusion for Multi-sensor 3D Object Detection , 2018, ECCV.

[13]  Klaus C. J. Dietmayer,et al.  Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[14]  Ji Wan,et al.  Multi-view 3D Object Detection Network for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yin Zhou,et al.  VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Julien Cornebise,et al.  Weight Uncertainty in Neural Networks , 2015, ArXiv.

[17]  Danfei Xu,et al.  PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Bin Yang,et al.  PIXOR: Real-time 3D Object Detection from Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Carlos Vallespi-Gonzalez,et al.  LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[21]  Kyungjae Lee,et al.  Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Alex Graves,et al.  Practical Variational Inference for Neural Networks , 2011, NIPS.

[23]  Julien Cornebise,et al.  Weight Uncertainty in Neural Network , 2015, ICML.

[24]  Gregory P. Meyer.  An Alternative Probabilistic Interpretation of the Huber Loss , 2019, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Jake Charland,et al.  Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[26]  Steven Lake Waslander,et al.  Joint 3D Proposal Generation and Object Detection from View Aggregation , 2017, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[27]  Bin Yang,et al.  HDNET: Exploiting HD Maps for 3D Object Detection , 2018, CoRL.

[28]  Klaus C. J. Dietmayer,et al.  Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection , 2018, 2019 IEEE Intelligent Vehicles Symposium (IV).