Machine Learning for Intelligent Transportation Systems
Li Erran Li · Trevor Darrell

Fri Dec 9th 08:00 AM -- 06:30 PM @ Room 124 + 125
Event URL: »

Our transportation systems are poised for a transformation as we make progress on autonomous vehicles, vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication infrastructures, and smart road infrastructures such as smart traffic lights. There are many challenges in transforming our current transportation systems to the future vision. For example, how do we achieve near-zero fatality? How do we optimize efficiency through intelligent traffic management and control of fleets? How do we optimize for traffic capacity during rush hours? To meet these requirements in safety, efficiency, control, and capacity, the systems must be automated with intelligent decision making.

Machine learning will be essential to enable intelligent transportation systems. Machine learning has made rapid progress in self-driving, e.g. real-time perception and prediction of traffic scenes, and has started to be applied to ride-sharing platforms such as Uber (e.g. demand forecasting) and crowd-sourced video scene analysis companies such as Nexar (understanding and avoiding accidents). To address the challenges arising in our future transportation system such as traffic management and safety, we need to consider the transportation systems as a whole rather than solving problems in isolation. New machine learning solutions are needed as transportation places specific requirements such as extremely low tolerance on uncertainty and the need to intelligently coordinate self-driving cars through V2V and V2X.

The goal of this workshop is to bring together researchers and practitioners from all areas of intelligent transportations systems to address core challenges with machine learning. These challenges include, but are not limited to: predictive modeling of risk and accidents through telematics, modeling, simulation and forecast of demand and mobility patterns in large scale urban transportation systems, machine learning approaches for control and coordination of traffic leveraging V2V and V2X infrastructures, efficient pedestrian detection, pedestrian intent detection, intelligent decision-making for self-driving cars, scene classification, real-time perception and prediction of traffic scenes, deep reinforcement learning from human drivers, uncertainty propagation in deep neural networks, efficient inference with deep neural networks.

The workshop will include invited speakers, panels, presentations of accepted papers and posters. We invite papers in the form of short, long and position papers to address the core challenges mentioned above. We encourage researchers and practitioners on self-driving cars, transportation systems and ride-sharing platforms to participate.

08:30 AM Opening Remarks (Talk)
08:45 AM Invited Talk: Safe Reinforcement Learning for Robotics (Pieter Abbeel, UC Berkeley and OpenAI) (Invited Talk) Pieter Abbeel
09:15 AM Invited Talk: Active Optimization and Autonomous Vehicles (Jeff Schneider, CMU and Uber ATC) (Invited Talk)
09:45 AM Contributed Talks (3 x 15 min) (Contributed Talks)
10:30 AM Posters and Break <span> <a href="#"></a> </span>
11:00 AM Invited Talk: Learning Affordance for Direct Perception in Autonomous Driving (JiaoXiong Xiao, AutoX) (Invited Talk)
11:30 AM Invited Talk: End to End Learning for Self-Driving Cars (Larry Jackel, NVIDIA) (Invited Talk)
12:00 PM Invited Talk: Towards Affordable Self-driving Cars (Raquel Urtasun, University of Toronto) (Invited Talk) Raquel Urtasun
12:30 PM Lunch (Break)
01:40 PM Invited Talk: Visual Understanding of Human Activities for Smart Vehicles and Interactive Environments (Juan Carlos Niebles, Stanford) (Invited Talk) Juan Carlos Niebles
02:10 PM Invited Talk: Autonomous Cars that Coordinate with People (Anca Dragan, Berkeley) (Invited Talk) Anca Dragan
02:40 PM Lightning Talks (6 x 2 min) (Lightning Talks)
03:00 PM Posters and Coffe (Posters and Break)
03:30 PM Invited Talk: Scene Labeling and more – Deep Neural Nets for Autonomous Vehicles (Uwe Franke, Daimler AG) (Invited Talk)
04:00 PM Invited Talk: Efficient Deep Networks for Real-Time Classification in Embedded Platforms (Jose Alvarez, NICTA, Australia) (Invited Talk)
04:30 PM Invited Talk: Domain Adaption for Perception and Action (Kate Saenko, Boston University) (Invited Talk) Kate Saenko
05:00 PM Invited Talk: Learning Adaptive Driving Models from Large-scale Video Datasets (Fisher Yu, Huazhe Xu, Dequan Wang, and Trevor Darrell, Berkeley) (Invited Talk) Trevor Darrell
05:30 PM Discussion <span> <a href="#"></a> </span>
06:00 PM Closing Remarks (Talk)

Author Information

Li Erran Li (

Li Erran Li is the head of machine learning at Scale and an adjunct professor at Columbia University. Previously, he was chief scientist at Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He’s an ACM Fellow and IEEE Fellow.

Trevor Darrell (UC Berkeley)

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