Jane Macfarlane

Real-time detection and classification of traffic jams from probe data

In this paper we present our experience on detecting and classifying traffic jams in real time from probe data. We classify traffic jams at two levels. At a higher level, we classify traffic jams into recurring and non-recurring jams. Then at a lower level we identify accidents out of non-recurring jams based on features that characterize upstream and downstream traffic patterns. Accidents are highly unpredictable and usually create heavy and long lasting congestion, and therefore are particularly worth detecting. We discuss the challenges of detecting accidents in real time as well as our approaches and results.

Addressing the uncertainties in autonomous driving

Autonomous driving is a highly complex sensing and control problem. Todays vehicles may include many different compositions of sensor sets including the newer more sophisticated sensors like radar, cameras, and lidar. Each sensor in the car provides specific information about the environment at varying levels and has an inherent uncertainty and accuracy measure. Beyond the sensors needed for perception, the control system needs some basic measure of its position in space and its surrounding reality that can be conflated with the perceived local space. The inherent challenges of map building introduce its own set of uncertainties. As such, the map itself can be regarded as a very complex sensor with multiple levels of uncertainty and measures of accuracy. The algorithms used to integrate this information will have to manage the propagation of their inaccuracies, fuse information to reduce the uncertainties and, in the end, offer levels of confidence in the produced representations that can be then used for safe navigation decisions and actions.