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Workshop: Machine Learning for Autonomous Driving

Rowan McAllister, Xinshuo Weng, Daniel Omeiza, Nick Rhinehart, Fisher Yu, German Ros, Vladlen Koltun

2020-12-11T07:55:00-08:00 - 2020-12-11T17:00:00-08:00
Abstract: 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.


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2020-12-11T07:55:00-08:00 - 2020-12-11T08:00:00-08:00
Rowan McAllister
2020-12-11T08:00:00-08:00 - 2020-12-11T08:30:00-08:00
Invited Talk: Patrick Perez
Patrick Pérez
2020-12-11T08:30:00-08:00 - 2020-12-11T08:40:00-08:00
Q&A: Patrick Perez
Patrick Pérez
2020-12-11T08:40:00-08:00 - 2020-12-11T09:20:00-08:00
Invited Talk: Angela Schoellig
Angela Schoellig
2020-12-11T09:20:00-08:00 - 2020-12-11T10:00:00-08:00
Break and Posters
2020-12-11T10:00:00-08:00 - 2020-12-11T10:40:00-08:00
Invited Talk: Jianxiong Xiao
Jianxiong Xiao
2020-12-11T10:40:00-08:00 - 2020-12-11T11:00:00-08:00
Invited Talk: Pin Wang
Pin Wang
2020-12-11T11:00:00-08:00 - 2020-12-11T11:10:00-08:00
Q&A: Pin Wang
Pin Wang
2020-12-11T11:10:00-08:00 - 2020-12-11T11:50:00-08:00
Invited Talk: Ehud Sharlin
Ehud Sharlin
2020-12-11T11:50:00-08:00 - 2020-12-11T12:00:00-08:00
Q&A: Ehud Sharlin
Ehud Sharlin
2020-12-11T12:00:00-08:00 - 2020-12-11T13:00:00-08:00
Break and Posters
2020-12-11T13:00:00-08:00 - 2020-12-11T13:30:00-08:00
Invited Talk: Byron Boots
Byron Boots
2020-12-11T13:30:00-08:00 - 2020-12-11T13:40:00-08:00
Q&A: Byron Boots
Byron Boots
2020-12-11T13:40:00-08:00 - 2020-12-11T14:10:00-08:00
Invited Talk: Brandyn White
Brandyn White
2020-12-11T14:10:00-08:00 - 2020-12-11T14:20:00-08:00
Q&A: Brandyn White
Brandyn White
2020-12-11T14:20:00-08:00 - 2020-12-11T15:00:00-08:00
Break and Posters
2020-12-11T15:00:00-08:00 - 2020-12-11T16:00:00-08:00
CARLA Challenge
German Ros
2020-12-11T16:00:00-08:00 - 2020-12-11T16:30:00-08:00
Invited Talk: Beipeng Mu
Beipeng Mu
2020-12-11T16:30:00-08:00 - 2020-12-11T16:40:00-08:00
Q&A: Beipeng Mu
Beipeng Mu
Paper 43: DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition in Large-Scale Changing Environments
Marvin Chancán Chancan
Paper 59: Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF)
Simon Isele
Paper 45: A Comprehensive Study on the Application of Structured Pruning methods in Autonomous Vehicles
Ibrahim Sobh, Ahmed Hamed
Paper 1: Multimodal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network
Rowan McAllister
Paper 13: Conditional Imitation Learning Driving Considering Camera and LiDAR Fusion
Hesham Eraqi
Paper 21: Haar Wavelet based Block Autoregressive Flows for Trajectories
Apratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz, Bernt Schiele
Paper 11: Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models
Nick Lamm, Iddo Drori
Paper 53: A Distributed Delivery-Fleet Management Framework using Deep Reinforcement Learning and Dynamic Multi-Hop Routing
Vaneet Aggarwal, Bharat Bhargava
Paper 33: Risk Assessment for Machine Learning Models
Fabian Hueger, Peter Schlicht
Paper 50: Diverse Sampling for Flow-Based Trajectory Forecasting
Jason Ma, Jeevana Priya Inala, Dinesh Jayaraman, Osbert Bastani
Paper 12: DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation
Rowan McAllister
Paper 56: IDE-Net: Extracting Interactive Driving Patterns from Human Data
Liting Sun, Wei Zhan
Paper 60: Traffic Forecasting using Vehicle-to-Vehicle Communication and Recurrent Neural Networks
Rose Yu
Paper 46: Disagreement-Regularized Imitation of Complex Multi-Agent Interactions
Jiaming Song, Stefano Ermon
Paper 44: CARLA Real Traffic Scenarios – novel training ground and benchmark for autonomous driving
Błażej Osiński, Piotr Miłoś, Adam Jakubowski, Christopher Galias, Silviu Homoceanu
Paper 58: Vehicle speed data imputation based on parameter transferred LSTM
JUNGMIN KWON, Hyunggon Park
Paper 57: Single Shot Multitask Pedestrian Detection and Behavior Prediction
Rowan McAllister
Paper 39: Bézier Curve Based End-to-End Trajectory Synthesis for Agile Autonomous Driving
Trent Weiss, Madhur Behl
Paper 10: Certified Interpretability Robustness for Class Activation Mapping
Alex Gu, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel
Paper 55: Physically Feasible Vehicle Trajectory Prediction
Jerrick Hoang, Micol Marchetti-Bowick
Paper 14: PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
Amir Rasouli, Mohsen Rohani
Paper 27: Explainable Autonomous Driving with Grounded Relational Inference
Nishan Srishankar, Sujitha Martin, Masayoshi Tomizuka
Paper 9: Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts
Tiago Azevedo, Matthew Mattina, Partha Maji
Paper 38: Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS
Flora Dellinger, Diego Mendoza Barrenechea, Isabelle Leang
Paper 41: Extracting Traffic Smoothing Controllers Directly From Driving Data using Offline RL
Eugene Vinitsky
Paper 40: Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks
Panagiotis Tigas, Yarin Gal
Paper 24: 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation
Shaul Oron
Paper 22: RAMP-CNN: A Novel Neural Network for EnhancedAutomotive Radar Object Recognition
Rowan McAllister
Paper 19: Multiagent Driving Policy for Congestion Reduction in a Large Scale Scenario
Jiaxun Cui, Peter Stone
Paper 30: MODETR: Moving Object Detection with Transformers
Ahmad El Sallab, Hazem Rashed
Paper 31: SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Changick Kim
Paper 15: Calibrating Self-supervised Monocular Depth Estimation
Rowan McAllister
Paper 52: Distributionally Robust Online Adaptation via Offline Population Synthesis
Aman Sinha, Matthew O'Kelly, Hongrui Zheng
Paper 6: FisheyeYOLO: Object Detection on Fisheye Cameras for Autonomous Driving
Hazem Rashed, Ahmad El Sallab
Paper 61: Predicting times of waiting on red signals using BERT
Paweł Gora, Witold Szejgis
Paper 20: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
YUXUAN CAI, Wei Niu, Yanzhi Wang
Paper 8: EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi
Paper 37: Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Abhishek Mohta, Fang-Chieh Chou, Brian C Becker, Nemanja Djuric, Carlos Vallespi
Paper 2: Energy-Based Continuous Inverse Optimal Control
Yifei Xu, Jianwen Xie, Chris Baker, Yibiao Zhao, Ying Nian Wu
Paper 42: Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization
Zhaoen Su, Nemanja Djuric, Carlos Vallespi, Dave Bradley
Paper 64: Modeling Affect-based Intrinsic Rewards for Exploration and Learning
Daniel McDuff, Ashish Kapoor
Paper 18: Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Carlos Vallespi, Nemanja Djuric
Paper 16: Driving Behavior Explanation with Multi-level Fusion
Matthieu Cord, Patrick Pérez
Paper 7: Real-time Semantic and Class-agnostic Instance Segmentation in Autonomous Driving
Mennatullah Siam, Hazem Rashed, Ahmad El Sallab
Paper 32: Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem with Nonlinear Dynamic Behavior Ramadan
Paper 49: ULTRA: A reinforcement learning generalization benchmark for autonomous driving
, Daniel Graves
Paper 62: Instance-wise Depth and Motion Learning from Monocular Videos
Seokju Lee, Sunghoon Im, Stephen Lin, In So Kweon
Paper 51: Multi-modal Agent Trajectory Prediction with Local Self-Attention Contexts
Manoj Bhat, Jonathan Francis