Self-Driving Cars - An AI-Robotics Challenge
نویسنده
چکیده
In recent years, all major automotive companies have launched initiatives towards cars that assist people in making driving decisions. The ultimate goal of all these efforts are cars that can drive themselves. The benefit of such a technology could be enormous. At present, some 42,000 people die every year in traffic accidents in the U.S., mostly because of human error. Self-driving cars could make people safer and more productive. Self-driving cars is a true AI challenge. To endow cars with the ability to make decisions on behalf of their drivers, they have to sense, perceive, and act. Recent work in this field has extensively built on probabilistic representations and machine learning methods. The speaker will report on past work on the DARPA Grand Challenge, and discuss ongoing work on the Urban Challenge, DARPA’s follow-up program on self-driving cars.
منابع مشابه
Winning the DARPA Grand Challenge with an AI Robot
This paper describes the software architecture of Stanley, an autonomous land vehicle developed for high-speed desert driving without human intervention. The vehicle recently won the DARPA Grand Challenge, a major robotics competition. The article describes the software architecture of the robot, which relied pervasively on state-of-the-art AI technologies, such as machine learning and probabil...
متن کاملLearning to Drive: Perception for Autonomous Cars a Dissertation Submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Every year, 1.2 million people die in automobile accidents and up to 50 million are injured [1]. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure r...
متن کاملComparison of Architectural Design Decisions for Resource-Constrained Self-Driving Cars - A Multiple Case-Study
Context: Self-Driving cars are getting more and more attention with public demonstration from all important automotive OEMs but also from companies, which do not have a long history in the automotive industry. Fostered by large international competitions in the last decade, several automotive OEMs have already announced to bring this technology to the market around 2020. Objective: Internationa...
متن کاملProbabilistic Terrain Analysis For High-Speed Desert Driving
The ability to perceive and analyze terrain is a key problem in mobile robot navigation. Terrain perception problems arise in planetary robotics, agriculture, mining, and, of course, self-driving cars. Here, we introduce the PTA (probabilistic terrain analysis) algorithm for terrain classification with a fastmoving robot platform. The PTA algorithm uses probabilistic techniques to integrate ran...
متن کاملMachine learning, social learning and the governance of self-driving cars.
Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. ...
متن کامل