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LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
G. P. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez, C. Wellington
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. […]
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Computer Vision and Pattern Recognition (CVPR), 2019
Evolvability ES: Scalable and Direct Optimization of Evolvability
A. Gajewski, J. Clune, K. O. Stanley, J. Lehman
Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and enables fast adaptation to changing circumstances. This paper introduces evolvability ES, an evolutionary algorithm designed to explicitly and efficiently optimize for evolvability, i.e. the ability to further adapt. […] [PDF]
The Genetic and Evolutionary Computation Conference (GECCO), 2019
Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks
D. Lee, Y. Gu, J. Hoang, M. Marchetti-Bowick
Using weakly intent label can potentially predict the interaction and the resulting trajectory better. We use a GNN to model the interaction. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
A. Jain, S. Casas, R. Liao, Y. Xiong, S. Feng, S. Segal, R. Urtasun
Our research shows that non-parametric distributions can capture extremely well the (erratic) pedestrian behavior. We propose Discrete Residual Flow, a convolutional neural network for human motion prediction that accurately models the temporal dependencies and captures the uncertainty inherent in long-range motion forecasting. In particular, our method captures multi-modal posteriors over future human motion very realistically. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Physically Realizable Adversarial Examples for LiDAR Object Detection
J. Tu, M.Ren, S.Manivasagam, B. Yang, M. Liang, R. Du, F.Cheng,
R. Urtasun
Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks have only been applied to 2D flat images. […] [PDF]
Computer Vision and Pattern Recognition
(CVPR), 2017
DeepRoadMapper: Extracting Road Topology From Aerial Images
G. Máttyus, W. Luo, R. Urtasun
Creating road maps is essential for applications such as autonomous driving and city planning. Most approaches in industry focus on leveraging expensive sensors mounted on top of a fleet of cars. This results in very accurate estimates when exploiting a user in the loop. […] [PDF]
International Conference on Computer Vision (ICCV), 2017
Inference in Probabilistic Graphical Models by Graph Neural Networks
K. Yoon, R. Liao, Y. Xiong, L. Zhang, E. Fetaya, R. Urtasun, R. Zemel, X. Pitkow
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. […] [PDF]
Workshop @ International Conference on Learning Representations (ICLR), 2018
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