Workshop
Fri Dec 11 07:55 AM -- 05:00 PM (PST)
Machine Learning for Autonomous Driving
Welcome to the NeurIPS 2020 Workshop on Machine Learning for Autonomous Driving!
Autonomous vehicles (AVs) offer a rich source of high-impact research problems for the machine learning (ML) community; including perception, state estimation, probabilistic modeling, time series forecasting, gesture recognition, robustness guarantees, real-time constraints, user-machine communication, multi-agent planning, and intelligent infrastructure. Further, the interaction between ML subfields towards a common goal of autonomous driving can catalyze interesting inter-field discussions that spark new avenues of research, which this workshop aims to promote. As an application of ML, autonomous driving has the potential to greatly improve society by reducing road accidents, giving independence to those unable to drive, and even inspiring younger generations with tangible examples of ML-based technology clearly visible on local streets.
All are welcome to submit and/or attend! This will be the 5th NeurIPS workshop in this series. Previous workshops in 2016, 2017, 2018 and 2019 enjoyed wide participation from both academia and industry.
| Welcome | |
| Invited Talk: Patrick Perez (Talk) | |
| Q&A: Patrick Perez (Q&A) | |
| Invited Talk: Angela Schoellig (Talk) | |
| Break and Posters (Break) | |
| Invited Talk: Jianxiong Xiao (Talk) | |
| Invited Talk: Pin Wang (Talk) | |
| Q&A: Pin Wang (Q&A) | |
| Invited Talk: Ehud Sharlin (Talk) | |
| Q&A: Ehud Sharlin (Q&A) | |
| Break and Posters (Break) | |
| Invited Talk: Byron Boots (Talk) | |
| Q&A: Byron Boots (Q&A) | |
| Invited Talk: Brandyn White (Talk) | |
| Q&A: Brandyn White (Q&A) | |
| Break and Posters (Break) | |
| CARLA Challenge (Talks) | |
| Invited Talk: Beipeng Mu (Talk) | |
| Q&A: Beipeng Mu (Q&A) | |
| Paper 60: Traffic Forecasting using Vehicle-to-Vehicle Communication and Recurrent Neural Networks (Contributed Talk) | |
| Paper 61: Predicting times of waiting on red signals using BERT (Contributed Talk) | |
| Paper 62: Instance-wise Depth and Motion Learning from Monocular Videos (Contributed Talk) | |
| Paper 64: Modeling Affect-based Intrinsic Rewards for Exploration and Learning (Contributed Talk) | |
| Paper 2: Energy-Based Continuous Inverse Optimal Control (Contributed Talk) | |
| Paper 6: FisheyeYOLO: Object Detection on Fisheye Cameras for Autonomous Driving (Contributed Talk) | |
| Paper 7: Real-time Semantic and Class-agnostic Instance Segmentation in Autonomous Driving (Contributed Talk) | |
| Paper 8: EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning (Contributed Talk) | |
| Paper 9: Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts (Contributed Talk) | |
| Paper 10: Certified Interpretability Robustness for Class Activation Mapping (Contributed Talk) | |
| Paper 12: DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation (Contributed Talk) | |
| Paper 13: Conditional Imitation Learning Driving Considering Camera and LiDAR Fusion (Contributed Talk) | |
| Paper 14: PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D (Contributed Talk) | |
| Paper 15: Calibrating Self-supervised Monocular Depth Estimation (Contributed Talk) | |
| Paper 16: Driving Behavior Explanation with Multi-level Fusion (Contributed Talk) | |
| Paper 18: Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction Models (Contributed Talk) | |
| Paper 19: Multiagent Driving Policy for Congestion Reduction in a Large Scale Scenario (Contributed Talk) | |
| Paper 20: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design (Contributed Talk) | |
| Paper 22: RAMP-CNN: A Novel Neural Network for EnhancedAutomotive Radar Object Recognition (Contributed Talk) | |
| Paper 24: 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation (Contributed Talk) | |
| Paper 27: Explainable Autonomous Driving with Grounded Relational Inference (Contributed Talk) | |
| Paper 38: Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS (Contributed Talk) | |
| Paper 31: SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction (Contributed Talk) | |
| Paper 32: Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem with Nonlinear Dynamic Behavior (Contributed Talk) | |
| Paper 11: Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models (Contributed Talk) | |
| Paper 43: DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition in Large-Scale Changing Environments (Contributed Talk) | |
| Paper 21: Haar Wavelet based Block Autoregressive Flows for Trajectories (Contributed Talk) | |
| Paper 1: Multimodal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network (Contributed Talk) | |
| Paper 51: Multi-modal Agent Trajectory Prediction with Local Self-Attention Contexts (Contributed Talk) | |
| Paper 30: MODETR: Moving Object Detection with Transformers (Contributed Talk) | |
| Paper 33: Risk Assessment for Machine Learning Models (Contributed Talk) | |
| Paper 37: Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models (Contributed Talk) | |
| Paper 39: Bézier Curve Based End-to-End Trajectory Synthesis for Agile Autonomous Driving (Contributed Talk) | |
| Paper 40: Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks (Contributed Talk) | |
| Paper 41: Extracting Traffic Smoothing Controllers Directly From Driving Data using Offline RL (Contributed Talk) | |
| Paper 42: Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization (Contributed Talk) | |
| Paper 44: CARLA Real Traffic Scenarios – novel training ground and benchmark for autonomous driving (Contributed Talk) | |
| Paper 45: A Comprehensive Study on the Application of Structured Pruning methods in Autonomous Vehicles (Contributed Talk) | |
| Paper 46: Disagreement-Regularized Imitation of Complex Multi-Agent Interactions (Contributed Talk) | |
| Paper 49: ULTRA: A reinforcement learning generalization benchmark for autonomous driving (Contributed Talk) | |
| Paper 50: Diverse Sampling for Flow-Based Trajectory Forecasting (Contributed Talk) | |
| Paper 52: Distributionally Robust Online Adaptation via Offline Population Synthesis (Contributed Talk) | |
| Paper 53: A Distributed Delivery-Fleet Management Framework using Deep Reinforcement Learning and Dynamic Multi-Hop Routing (Contributed Talk) | |
| Paper 55: Physically Feasible Vehicle Trajectory Prediction (Contributed Talk) | |
| Paper 56: IDE-Net: Extracting Interactive Driving Patterns from Human Data (Contributed Talk) | |
| Paper 57: Single Shot Multitask Pedestrian Detection and Behavior Prediction (Contributed Talk) | |
| Paper 58: Vehicle speed data imputation based on parameter transferred LSTM (Contributed Talk) | |
| Paper 59: Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF) (Contributed Talk) | |
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