We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) to assist unmanned ground vehicles in performing path planning within dynamic environments. In addition to predicting the location of moving objects in the environment, we have extended PRIDE to generate simulated traffic during on-road driving. In this paper, we explore applying the PRIDE-based traffic control algorithms for the performance evaluation of autonomous vehicles. Through the use of repeatable and realistic traffic simulation, one is able to evaluate the performance of an autonomous vehicle in an on-road driving scenario without the risk involved with introducing the vehicle into a potentially dangerous roadway situation. In addition, by varying a single vehicle?s parameters (such as aggressivity), we can show how the entire traffic pattern is affected. We will describe the successes that have been achieved to date in a simulated environment, as well as enhancements that are currently being researched and expected in the near future.
Citation: Submitted to the Integrated Computer-Aided Engineering Journal
Pub Type: Journals
autonomous navigations, Knowledge Engineering, Mobility, moving object prediction, Performance Metrics, performance metrics, PRIDE