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Workshop
Machine Learning for Autonomous Driving
Rowan McAllister · Xinshuo Weng · Daniel Omeiza · Nick Rhinehart · Fisher Yu · German Ros · Vladlen Koltun

Fri Dec 11 07:55 AM -- 05:00 PM (PST) @ None
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.

Fri 7:55 a.m. - 8:00 a.m.
Welcome  link » Rowan McAllister
Fri 8:00 a.m. - 8:30 a.m.
Invited Talk: Patrick Perez (Talk)   
Patrick Pérez
Fri 8:30 a.m. - 8:40 a.m.
Q&A: Patrick Perez (Q&A)
Patrick Pérez
Fri 8:40 a.m. - 9:20 a.m.
Invited Talk: Angela Schoellig (Talk)
Angela Schoellig
Fri 9:20 a.m. - 10:00 a.m.
(Break)  link »

https://neurips.gather.town/app/RhCLTvx08wOwYaga/ml4ad

Fri 10:00 a.m. - 10:40 a.m.
Invited Talk: Jianxiong Xiao (Talk)   
Jianxiong Xiao
Fri 10:40 a.m. - 11:00 a.m.
Invited Talk: Pin Wang (Talk)   
Pin Wang
Fri 11:00 a.m. - 11:10 a.m.
Q&A: Pin Wang (Q&A)
Pin Wang
Fri 11:10 a.m. - 11:50 a.m.
Invited Talk: Ehud Sharlin (Talk)   
Ehud Sharlin
Fri 11:50 a.m. - 12:00 p.m.
Q&A: Ehud Sharlin (Q&A)
Ehud Sharlin
Fri 12:00 p.m. - 1:00 p.m.
(Break)  link »

https://neurips.gather.town/app/RhCLTvx08wOwYaga/ml4ad

Fri 1:00 p.m. - 1:30 p.m.
Invited Talk: Byron Boots (Talk)   
Byron Boots
Fri 1:30 p.m. - 1:40 p.m.
Q&A: Byron Boots (Q&A)
Byron Boots
Fri 1:40 p.m. - 2:10 p.m.
Invited Talk: Brandyn White (Talk)   
Brandyn White
Fri 2:10 p.m. - 2:20 p.m.
Q&A: Brandyn White (Q&A)
Brandyn White
Fri 2:20 p.m. - 3:00 p.m.
(Break)  link »

https://neurips.gather.town/app/RhCLTvx08wOwYaga/ml4ad

Fri 3:00 p.m. - 4:00 p.m.
(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
Fri 4:00 p.m. - 4:30 p.m.
Invited Talk: Beipeng Mu (Talk)   
Beipeng Mu
Fri 4:30 p.m. - 4:40 p.m.
Q&A: Beipeng Mu (Q&A)
Beipeng Mu
-
Paper 1: Multimodal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network (Contributed Talk)   
Rowan McAllister
-
Paper 2: Energy-Based Continuous Inverse Optimal Control (Contributed Talk)   
Yifei Xu · Jianwen Xie · Chris Baker · Yibiao Zhao · Ying Nian Wu
-
Paper 6: FisheyeYOLO: Object Detection on Fisheye Cameras for Autonomous Driving (Contributed Talk)   
Hazem Rashed · Ahmad El Sallab
-
Paper 7: Real-time Semantic and Class-agnostic Instance Segmentation in Autonomous Driving (Contributed Talk)   
Mennatullah Siam · Hazem Rashed · Ahmad El Sallab
-
Paper 8: EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning (Contributed Talk)   
Jiachen Li · Fan Yang · Masayoshi Tomizuka · Chiho Choi
-
Paper 9: Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts (Contributed Talk)   
Tiago Azevedo · Matthew Mattina · Partha Maji
-
Paper 10: Certified Interpretability Robustness for Class Activation Mapping (Contributed Talk)   
Alex Gu · Lily Weng · Pin-Yu Chen · Sijia Liu · Luca Daniel
-
Paper 11: Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models (Contributed Talk)   
Nick Lamm · Iddo Drori
-
Paper 12: DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation (Contributed Talk)   
Rowan McAllister
-
Paper 13: Conditional Imitation Learning Driving Considering Camera and LiDAR Fusion (Contributed Talk)   
Hesham Eraqi
-
Paper 14: PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D (Contributed Talk)   
Amir Rasouli · Mohsen Rohani
-
Paper 15: Calibrating Self-supervised Monocular Depth Estimation (Contributed Talk)   
Rowan McAllister
-
Paper 16: Driving Behavior Explanation with Multi-level Fusion (Contributed Talk)   
Matthieu Cord · Patrick Pérez
-
Paper 18: Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction Models (Contributed Talk)   
Carlos Vallespi · Nemanja Djuric
-
Paper 19: Multiagent Driving Policy for Congestion Reduction in a Large Scale Scenario (Contributed Talk)   
Jiaxun Cui · Peter Stone
-
Paper 20: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design (Contributed Talk)   
YUXUAN CAI · Wei Niu · Yanzhi Wang
-
Paper 21: Haar Wavelet based Block Autoregressive Flows for Trajectories (Contributed Talk)   
Apratim Bhattacharyya · Christoph-Nikolas Straehle · Mario Fritz · Bernt Schiele
-
Paper 22: RAMP-CNN: A Novel Neural Network for EnhancedAutomotive Radar Object Recognition (Contributed Talk)   
Rowan McAllister
-
Paper 24: 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation (Contributed Talk)   
Shaul Oron
-
Paper 27: Explainable Autonomous Driving with Grounded Relational Inference (Contributed Talk)   
Nishan Srishankar · Sujitha Martin · Masayoshi Tomizuka
-
Paper 30: MODETR: Moving Object Detection with Transformers (Contributed Talk)   
Ahmad El Sallab · Hazem Rashed
-
Paper 31: SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction (Contributed Talk)   
Changick Kim
-
Paper 32: Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem with Nonlinear Dynamic Behavior (Contributed Talk)   
https://www.facebook.com/amr.farag.370 Ramadan
-
Paper 33: Risk Assessment for Machine Learning Models (Contributed Talk)   
Fabian Hueger · Peter Schlicht
-
Paper 37: Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models (Contributed Talk)   
Abhishek Mohta · Fang-Chieh Chou · Brian C Becker · Nemanja Djuric · Carlos Vallespi
-
Paper 38: Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS (Contributed Talk)   
Flora Dellinger · Diego Mendoza Barrenechea · Isabelle Leang
-
Paper 39: Bézier Curve Based End-to-End Trajectory Synthesis for Agile Autonomous Driving (Contributed Talk)   
Trent Weiss · Madhur Behl
-
Paper 40: Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks (Contributed Talk)   
Panagiotis Tigas · Yarin Gal
-
Paper 41: Extracting Traffic Smoothing Controllers Directly From Driving Data using Offline RL (Contributed Talk)   
Eugene Vinitsky
-
Paper 42: Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization (Contributed Talk)   
Zhaoen Su · Nemanja Djuric · Carlos Vallespi · Dave Bradley
-
Paper 43: DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition in Large-Scale Changing Environments (Contributed Talk)
Marvin Chancán Chancan
-
Paper 44: CARLA Real Traffic Scenarios – novel training ground and benchmark for autonomous driving (Contributed Talk)   
Błażej Osiński · Piotr Miłoś · Adam Jakubowski · Christopher Galias · Silviu Homoceanu
-
Paper 45: A Comprehensive Study on the Application of Structured Pruning methods in Autonomous Vehicles (Contributed Talk)   
Ibrahim Sobh · Ahmed Hamed
-
Paper 46: Disagreement-Regularized Imitation of Complex Multi-Agent Interactions (Contributed Talk)   
Jiaming Song · Stefano Ermon
-
Paper 49: ULTRA: A reinforcement learning generalization benchmark for autonomous driving (Contributed Talk)   
xiru.zhu · Daniel Graves
-
Paper 50: Diverse Sampling for Flow-Based Trajectory Forecasting (Contributed Talk)   
Jason Ma · Jeevana Priya Inala · Dinesh Jayaraman · Osbert Bastani
-
Paper 51: Multi-modal Agent Trajectory Prediction with Local Self-Attention Contexts (Contributed Talk)
Manoj Bhat · Jonathan Francis
-
Paper 52: Distributionally Robust Online Adaptation via Offline Population Synthesis (Contributed Talk)   
Aman Sinha · Matthew O'Kelly · Hongrui Zheng
-
Paper 53: A Distributed Delivery-Fleet Management Framework using Deep Reinforcement Learning and Dynamic Multi-Hop Routing (Contributed Talk)   
Vaneet Aggarwal · Bharat Bhargava
-
Paper 55: Physically Feasible Vehicle Trajectory Prediction (Contributed Talk)   
Jerrick Hoang · Micol Marchetti-Bowick
-
Paper 56: IDE-Net: Extracting Interactive Driving Patterns from Human Data (Contributed Talk)   
Liting Sun · Wei Zhan
-
Paper 57: Single Shot Multitask Pedestrian Detection and Behavior Prediction (Contributed Talk)   
Rowan McAllister
-
Paper 58: Vehicle speed data imputation based on parameter transferred LSTM (Contributed Talk)   
JUNGMIN KWON · Hyunggon Park
-
Paper 59: Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF) (Contributed Talk)   
Simon Isele
-
Paper 60: Traffic Forecasting using Vehicle-to-Vehicle Communication and Recurrent Neural Networks (Contributed Talk)   
Rose Yu
-
Paper 61: Predicting times of waiting on red signals using BERT (Contributed Talk)   
Paweł Gora · Witold Szejgis
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Paper 62: Instance-wise Depth and Motion Learning from Monocular Videos (Contributed Talk)   
Seokju Lee · Sunghoon Im · Stephen Lin · In So Kweon
-
Paper 64: Modeling Affect-based Intrinsic Rewards for Exploration and Learning (Contributed Talk)   
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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