The Machine Learning for Intelligent Transportation Systems (MLITS) will be held in conjunction with NIPS 2016 on Dec 9, 2016 in Barcelona, SPAIN

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.