( events)   Timezone: »  
Program Highlights »
Sat Dec 09 08:00 AM -- 06:30 PM (PST) @ 201 A
2017 NIPS Workshop on Machine Learning for Intelligent Transportation Systems
Li Erran Li · Anca Dragan · Juan Carlos Niebles · Silvio Savarese

Workshop Home Page

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 to make perception accurate and robust to accomplish safe autonomous driving? How to learn long term driving strategies (known as driving policies) so that autonomous vehicles can be equipped with adaptive human negotiation skills when merging, overtaking and giving way, etc? 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
accurate and efficient pedestrian detection, pedestrian intent detection,
machine learning for object tracking,
unsupervised representation learning for autonomous driving,
deep reinforcement learning for learning driving policies,
cross-modal and simulator to real-world transfer learning,
scene classification, real-time perception and prediction of traffic scenes,
uncertainty propagation in deep neural networks,
efficient inference with deep neural networks
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,

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. Since this is a topic of broad and current interest, we expect at least 150 participants from leading university researchers, auto-companies and ride-sharing companies.

Opening Remarks (Openning)
Li Erran Li
Machine Learning for Self-Driving Cars, Raquel Urtasun, Uber ATG and University of Toronto (Invited Talk)
Raquel Urtasun
Exhausting the Sim with Domain Randomization and Trying to Exhaust the Real World, Pieter Abbeel, UC Berkeley and Embodied Intelligence (Invited Talk)
Pieter Abbeel, Greg Kahn
Learning-based system identification and decision making for autonomous driving, Marin Kobilarov, Zoox (Invited Talk)
Marin Kobilarov
Poster and Coffee (Coffee)
Hesham M. Eraqi, Mohamed N. Moustafa, Jens Honer, End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies (Contributed Talk)
Abhinav Jauhri (CMU), Carlee Joe-Wong, John Paul Shen, On the Real-time Vehicle Placement Problem (Contributed Talk)
Abhinav Jauhri, John Shen
Andrew Best (UNC), Sahil Narang, Lucas Pasqualin, Daniel Barber, Dinesh Manocha, AutonoVi-Sim: Autonomous Vehicle Simulation Platform with Weather, Sensing, and Traffic control (Contributed Talk)
Andrew Best
Nikita Japuria (MIT), Golnaz Habibi, Jonathan P. How, CASNSC: A context-based approach for accurate pedestrian motion prediction at intersections (Contributed Talk)
Nikita Jaipuria
6 Spotlight Talks (3 min each) (Contributed Talks)
Mennatullah Siam, Mohit Prabhushankar, Priyam Parashar, Mustafa Mukadam, hengshuai yao, Ransalu Senanayake
Adaptive Deep Learning for Perception, Action, and Explanation, Trevor Darrell (UC Berkeley) (Invited Talk)
Trevor Darrell
The challenges of applying machine learning to autonomous vehicles, Andrej Karpathy, Telsa (Invited Talk)
Micro-Perception Approach to Intelligent Transport, Ramesh Sarukkai (Lyft) (Invited Talk)
Ramesh Sarukkai
Posters and Coffe (Coffee)
On Autonomous Driving: Challenges and Opportunities, Sertac Karaman, MIT (Invited Talk)
Sertac Karaman
Generative Adversarial Imitation Learning, Stefano Ermon, Stanford (Invited Talk)
Stefano Ermon
Deep Learning and photo-realistic Simulation for real-world Machine Intelligence in Autonomous Driving, Adrien Gaidon (TRI) (Invited Talk)
Adrien Gaidon
Panel Discussion (Panel)
Greg Kahn, Ramesh Sarukkai, Adrien Gaidon, Sertac Karaman