Traffic Models for Self-driving Connected Cars

Self-Driving Cars

Self-Driving Cars - An AI-Robotics Challenge

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 ...

Active Optimization and Self Driving Cars

Self driving cars hold the promise of making transportation safer and more efficient than ever before possible. They will also be among the most complex robotic systems ever fielded and thus require an unprecedented level of machine learning throughout to achieve their desired performance. These learners create an everchanging environment for all algorithms operating in the system and optimizin...

A Philosophy for Developing Trust in Self‐ Driving Cars

For decades, our lives have depended on the safe operation of automated mechanisms around and inside us. The autonomy and complexity of these mechanisms is increasing dramatically. Autonomous systems such as self-driving cars rely heavily on inductive inference and complex software, both of which confound traditional software-safety techniques that are focused on amassing sufficient confirmator...

Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach

It has been well recognized that detecting drivable area is central to self-driving cars. Most of existing methods attempt to locate road surface by using lane line, thereby restricting to drivable area on which have a clear lane mark. This paper proposes an unsupervised approach for detecting drivable area utilizing both image data from a monocular camera and point cloud data from a 3D-LIDAR s...