Timezone: »
Workshop
Machine Learning for Autonomous Driving
Rowan McAllister · Xinshuo Weng · Daniel Omeiza · Nick Rhinehart · Fisher Yu · German Ros · Vladlen Koltun
Event URL: https://ml4ad.github.io/ »
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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Fri 7:55 a.m. - 8:00 a.m.
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Welcome link » | Rowan McAllister 🔗 |
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Fri 8:00 a.m. - 8:30 a.m.
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Invited Talk: Patrick Perez
(Talk)
SlidesLive Video » |
Patrick Pérez 🔗 |
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Fri 8:30 a.m. - 8:40 a.m.
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Q&A: Patrick Perez
(Q&A)
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Patrick Pérez 🔗 |
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Fri 8:40 a.m. - 9:20 a.m.
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Invited Talk: Angela Schoellig
(Talk)
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Angela Schoellig 🔗 |
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Fri 9:20 a.m. - 10:00 a.m.
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Break and Posters
(Break)
link »
https://neurips.gather.town/app/RhCLTvx08wOwYaga/ml4ad |
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Fri 10:00 a.m. - 10:40 a.m.
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Invited Talk: Jianxiong Xiao
(Talk)
SlidesLive Video » |
Jianxiong Xiao 🔗 |
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Fri 10:40 a.m. - 11:00 a.m.
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Invited Talk: Pin Wang
(Talk)
SlidesLive Video » |
Pin Wang 🔗 |
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Fri 11:00 a.m. - 11:10 a.m.
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Q&A: Pin Wang
(Q&A)
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Pin Wang 🔗 |
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Fri 11:10 a.m. - 11:50 a.m.
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Invited Talk: Ehud Sharlin
(Talk)
SlidesLive Video » |
Ehud Sharlin 🔗 |
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Fri 11:50 a.m. - 12:00 p.m.
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Q&A: Ehud Sharlin
(Q&A)
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Ehud Sharlin 🔗 |
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Fri 12:00 p.m. - 1:00 p.m.
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Break and Posters
(Break)
link »
https://neurips.gather.town/app/RhCLTvx08wOwYaga/ml4ad |
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Fri 1:00 p.m. - 1:30 p.m.
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Invited Talk: Byron Boots
(Talk)
SlidesLive Video » |
Byron Boots 🔗 |
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Fri 1:30 p.m. - 1:40 p.m.
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Q&A: Byron Boots
(Q&A)
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Byron Boots 🔗 |
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Fri 1:40 p.m. - 2:10 p.m.
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Invited Talk: Brandyn White
(Talk)
SlidesLive Video » |
Brandyn White 🔗 |
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Fri 2:10 p.m. - 2:20 p.m.
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Q&A: Brandyn White
(Q&A)
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Brandyn White 🔗 |
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Fri 2:20 p.m. - 3:00 p.m.
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Break and Posters
(Break)
link »
https://neurips.gather.town/app/RhCLTvx08wOwYaga/ml4ad |
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Fri 3:00 p.m. - 4:00 p.m.
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CARLA Challenge
(Talks)
link »
The CARLA Autonomous Driving Challenge 2020 is organized as part of the Machine Learning for Autonomous Driving Workshop at NeurIPS 2020. This competition is open to any participant from academia and industry. The challenge follows the same structure and rules defined for the CARLA AD Leaderboard. You can participate in any of the two available tracks: SENSORS and MAP, using the canonical sensors available for the challenge. The top-1 submissions of each track will be invited to present their results at the Machine Learning for Autonomous Driving Workshop. Additionally, all participants are invited to submit a technical report (up to 4 pages) describing their submissions. Based on the novelty and originality of these technical reports, the organization will select up to two teams to present their work at the workshop. |
German Ros 🔗 |
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Fri 4:00 p.m. - 4:30 p.m.
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Invited Talk: Beipeng Mu
(Talk)
SlidesLive Video » |
Beipeng Mu 🔗 |
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Fri 4:30 p.m. - 4:40 p.m.
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Q&A: Beipeng Mu
(Q&A)
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Beipeng Mu 🔗 |
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Paper 1: Multimodal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network
(Contributed Talk)
SlidesLive Video » |
Rowan McAllister 🔗 |
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Paper 2: Energy-Based Continuous Inverse Optimal Control
(Contributed Talk)
SlidesLive Video » |
Yifei Xu · Jianwen Xie · Chris Baker · Yibiao Zhao · Ying Nian Wu 🔗 |
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Paper 6: FisheyeYOLO: Object Detection on Fisheye Cameras for Autonomous Driving
(Contributed Talk)
SlidesLive Video » |
Hazem Rashed · Ahmad El Sallab 🔗 |
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Paper 7: Real-time Semantic and Class-agnostic Instance Segmentation in Autonomous Driving
(Contributed Talk)
SlidesLive Video » |
Mennatullah Siam · Hazem Rashed · Ahmad El Sallab 🔗 |
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Paper 8: EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
(Contributed Talk)
SlidesLive Video » |
Jiachen Li · Fan Yang · Masayoshi Tomizuka · Chiho Choi 🔗 |
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Paper 9: Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts
(Contributed Talk)
SlidesLive Video » |
Tiago Azevedo · Matthew Mattina · Partha Maji 🔗 |
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Paper 10: Certified Interpretability Robustness for Class Activation Mapping
(Contributed Talk)
SlidesLive Video » |
Alex Gu · Tsui-Wei Weng · Pin-Yu Chen · Sijia Liu · Luca Daniel 🔗 |
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Paper 11: Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models
(Contributed Talk)
SlidesLive Video » |
Nick Lamm · Iddo Drori 🔗 |
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Paper 12: DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation
(Contributed Talk)
SlidesLive Video » |
Rowan McAllister 🔗 |
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Paper 13: Conditional Imitation Learning Driving Considering Camera and LiDAR Fusion
(Contributed Talk)
SlidesLive Video » |
Hesham Eraqi 🔗 |
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Paper 14: PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
(Contributed Talk)
SlidesLive Video » |
Amir Rasouli · Mohsen Rohani 🔗 |
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Paper 15: Calibrating Self-supervised Monocular Depth Estimation
(Contributed Talk)
SlidesLive Video » |
Rowan McAllister 🔗 |
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Paper 16: Driving Behavior Explanation with Multi-level Fusion
(Contributed Talk)
SlidesLive Video » |
Matthieu Cord · Patrick Pérez 🔗 |
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Paper 18: Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
(Contributed Talk)
SlidesLive Video » |
Carlos Vallespi · Nemanja Djuric 🔗 |
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Paper 19: Multiagent Driving Policy for Congestion Reduction in a Large Scale Scenario
(Contributed Talk)
SlidesLive Video » |
Jiaxun Cui · Peter Stone 🔗 |
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Paper 20: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
(Contributed Talk)
SlidesLive Video » |
YUXUAN CAI · Wei Niu · Yanzhi Wang 🔗 |
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Paper 21: Haar Wavelet based Block Autoregressive Flows for Trajectories
(Contributed Talk)
SlidesLive Video » |
Apratim Bhattacharyya · Christoph-Nikolas Straehle · Mario Fritz · Bernt Schiele 🔗 |
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Paper 22: RAMP-CNN: A Novel Neural Network for EnhancedAutomotive Radar Object Recognition
(Contributed Talk)
SlidesLive Video » |
Rowan McAllister 🔗 |
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Paper 24: 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation
(Contributed Talk)
SlidesLive Video » |
Shaul Oron 🔗 |
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Paper 27: Explainable Autonomous Driving with Grounded Relational Inference
(Contributed Talk)
SlidesLive Video » |
Nishan Srishankar · Sujitha Martin · Masayoshi Tomizuka 🔗 |
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Paper 30: MODETR: Moving Object Detection with Transformers
(Contributed Talk)
SlidesLive Video » |
Ahmad El Sallab · Hazem Rashed 🔗 |
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Paper 31: SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
(Contributed Talk)
SlidesLive Video » |
Changick Kim 🔗 |
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Paper 32: Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem with Nonlinear Dynamic Behavior
(Contributed Talk)
SlidesLive Video » |
Amr Ramadan 🔗 |
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Paper 33: Risk Assessment for Machine Learning Models
(Contributed Talk)
SlidesLive Video » |
Fabian Hueger · Peter Schlicht 🔗 |
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Paper 37: Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
(Contributed Talk)
SlidesLive Video » |
Abhishek Mohta · Fang-Chieh Chou · Brian C Becker · Nemanja Djuric · Carlos Vallespi 🔗 |
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Paper 38: Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS
(Contributed Talk)
SlidesLive Video » |
Flora Dellinger · Diego Mendoza Barrenechea · Isabelle Leang 🔗 |
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Paper 39: Bézier Curve Based End-to-End Trajectory Synthesis for Agile Autonomous Driving
(Contributed Talk)
SlidesLive Video » |
Trent Weiss · Madhur Behl 🔗 |
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Paper 40: Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks
(Contributed Talk)
SlidesLive Video » |
Panagiotis Tigas · Yarin Gal 🔗 |
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Paper 41: Extracting Traffic Smoothing Controllers Directly From Driving Data using Offline RL
(Contributed Talk)
SlidesLive Video » |
Eugene Vinitsky 🔗 |
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Paper 42: Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization
(Contributed Talk)
SlidesLive Video » |
Zhaoen Su · Nemanja Djuric · Carlos Vallespi · David Bradley 🔗 |
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Paper 43: DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition in Large-Scale Changing Environments
(Contributed Talk)
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Marvin Chancan 🔗 |
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Paper 44: CARLA Real Traffic Scenarios – novel training ground and benchmark for autonomous driving
(Contributed Talk)
SlidesLive Video » |
Błażej Osiński · Piotr Miłoś · Adam Jakubowski · Krzysztof Galias · Silviu Homoceanu 🔗 |
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Paper 45: A Comprehensive Study on the Application of Structured Pruning methods in Autonomous Vehicles
(Contributed Talk)
SlidesLive Video » |
Ibrahim Sobh · Ahmed Hamed 🔗 |
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Paper 46: Disagreement-Regularized Imitation of Complex Multi-Agent Interactions
(Contributed Talk)
SlidesLive Video » |
Jiaming Song · Stefano Ermon 🔗 |
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Paper 49: ULTRA: A reinforcement learning generalization benchmark for autonomous driving
(Contributed Talk)
SlidesLive Video » |
Xiru Zhu · Daniel Graves 🔗 |
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Paper 50: Diverse Sampling for Flow-Based Trajectory Forecasting
(Contributed Talk)
SlidesLive Video » |
Jason Yecheng Ma · Jeevana Priya Inala · Dinesh Jayaraman · Osbert Bastani 🔗 |
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Paper 51: Multi-modal Agent Trajectory Prediction with Local Self-Attention Contexts
(Contributed Talk)
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Manoj Bhat · Jonathan Francis 🔗 |
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Paper 52: Distributionally Robust Online Adaptation via Offline Population Synthesis
(Contributed Talk)
SlidesLive Video » |
Aman Sinha · Matthew O'Kelly · Hongrui Zheng 🔗 |
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Paper 53: A Distributed Delivery-Fleet Management Framework using Deep Reinforcement Learning and Dynamic Multi-Hop Routing
(Contributed Talk)
SlidesLive Video » |
Vaneet Aggarwal · Bharat Bhargava 🔗 |
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Paper 55: Physically Feasible Vehicle Trajectory Prediction
(Contributed Talk)
SlidesLive Video » |
Jerrick Hoang · Micol Marchetti-Bowick 🔗 |
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Paper 56: IDE-Net: Extracting Interactive Driving Patterns from Human Data
(Contributed Talk)
SlidesLive Video » |
Liting Sun · Wei Zhan 🔗 |
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Paper 57: Single Shot Multitask Pedestrian Detection and Behavior Prediction
(Contributed Talk)
SlidesLive Video » |
Rowan McAllister 🔗 |
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Paper 58: Vehicle speed data imputation based on parameter transferred LSTM
(Contributed Talk)
SlidesLive Video » |
Jungmin Kwon · Hyunggon Park 🔗 |
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Paper 59: Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF)
(Contributed Talk)
SlidesLive Video » |
Simon Isele 🔗 |
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Paper 60: Traffic Forecasting using Vehicle-to-Vehicle Communication and Recurrent Neural Networks
(Contributed Talk)
SlidesLive Video » |
Rose Yu 🔗 |
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Paper 61: Predicting times of waiting on red signals using BERT
(Contributed Talk)
SlidesLive Video » |
Paweł Gora · Witold Szejgis 🔗 |
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Paper 62: Instance-wise Depth and Motion Learning from Monocular Videos
(Contributed Talk)
SlidesLive Video » |
Seokju Lee · Sunghoon Im · Stephen Lin · In So Kweon 🔗 |
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Paper 64: Modeling Affect-based Intrinsic Rewards for Exploration and Learning
(Contributed Talk)
SlidesLive Video » |
Daniel McDuff · Ashish Kapoor 🔗 |
Author Information
Rowan McAllister (UC Berkeley)
Xinshuo Weng (Carnegie Mellon University)
Daniel Omeiza (University of Oxford)
Nick Rhinehart (UC Berkeley)
Fisher Yu (ETH Zurich)
German Ros (Intel Labs)
Vladlen Koltun (Intel Labs)
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