This challenge tackles a key problem in the transportation world: How to efficiently manage dense traffic on complex railway networks? This is a real-world problem faced by many transportation and logistics companies around the world such as the Swiss Federal Railways and Deutsche Bahn. Your contribution may shape the way modern traffic management systems are implemented, not only in railway but also in other areas of transportation and logistics! The Flatland Challenge is held jointly with AMLD 2021.Compete
Huawei, as one of the largest global communication device vendors, manufactures billions of productions in hundreds of factories every year. A large amount of cargoes need to be delivered among factories during the manufacturing. Due to the uncertainties of customers’ requirements and production processes, most delivery requirements cannot be fully decided beforehand. The delivery orders, with the information including the pickup factories, delivery factories, the amount of cargoes and the time requirement, occur randomly and a fleet of homogeneous vehicles is periodically scheduled to serve these orders. Due to the large amount of deliver requests, even a small improvement of the logistics efficiency can bring significant benefits. Participate in this challenge to develop efficient optimization algorithms to dispatch orders and plan the route of vehicles.
In the field of autonomous driving, it is a consensus in both academia and industry that behavior prediction (e.g., trajectories, actions, intentions) is one of the most challenging problems blocking the realization of full autonomy. The problem cannot be solved without support from real-world motion data containing highly interactive behavior, as well as proper evaluation metrics and approaches for a variety of prediction algorithms based on the data. Unfortunately, there are yet no such a benchmark to fairly compare the performance of different prediction models/algorithms, particularly when the influence of prediction performance in a closed-loop format (integrated with different planners) is considered. The Mechanical Systems Control Laboratory (MSC Lab) at UC Berkeley has constructed an INTERnational, Adversarial and Cooperative moTION dataset (INTERACTION dataset) with collaborators from KIT and MINES ParisTech. It accurately recovers large amounts of highly interactive motions of road users (e.g., vehicles, pedestrians) in a variety of driving scenarios from different countries. To expedite research and inspire discussions on the evaluation of prediction models/algorithms, we present the INTERACTION-Dataset-based PREdicTion Challenge (INTERPRET). This is a step towards the construction of effective and valuable predictors for the development of autonomous driving.
The competition will begin soon but in the meantime check out an earlier version of the competition at NeurIPS 2020 here.
On the way towards a sustainable future and following up the success of L2RPN 2020 NeurIPS competition, this competition aims at unleashing the power of artificial intelligence even further for our real-world industrial application: controlling electricity power transmission in real-time and moving closer to truly “smart” grids using underutilized flexibilities. In 2020, participants were asked to develop an agent to be robust to unexpected events and keep delivering reliable electricity everywhere even in difficult circumstances. In this competition, participants, while dealing with a higher penetration of renewable energy, will be asked in addition to design trustworthy agents that are able to communicate when they are in trouble, especially when they might fail. This will more concretely lead the path towards an AI assistant for human operators, who will still be responsible for managing the grid, rather than a mere blackbox agent. Join us for this Augmented Intelligence competition!