Combination of Car Following and Lane Changing Models as a Drivers' Optimization Process

Ajot

Madhavan³

2005

ICA

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.

Section: Statistical Methods For Estimating Obstacle Locations Using mentioning

confidence: 99%

Performance evaluation of autonomous vehicle navigation in dynamic, on-road environments

Ajot

Madhavan³

2005

ICA

“…Some of those postulates can be expanded to generic situations. Additional work preformed at the Sharif University of Technology of Tehran [4] is based on two assumptions: the maximum speed on a road segment and the potential danger that could be incurred by traversing this road segment. These assumptions and concepts allow the simulation program to create a rank of actions the vehicle can execute based on the danger that could be incurred.…”

Section: Introductionmentioning

confidence: 99%

Prediction in Dynamic Environments for Autonomous On-Road Driving

Madhavan

Kootbally

2006 9th International Conference on Control, Automation, Robotics and Vision

2006

We have developed PRIDE (Prediction In Dynamic Environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE incorporates a long-term (LT) prediction approach based on situation recognition and a short-term (ST) prediction approach based on vehicle models. These two approaches provide for the prediction of the future location of moving objects at various levels of resolution at the frequency and level of abstraction necessary for planners at different levels within the hierarchy using sensory data. In this paper, we demonstrate the ability to use the results of the short-term prediction algorithms to strengthen/weaken the estimates of the long-term prediction algorithms via experimental results. We provide experimental results in an autonomous onroad driving scenario using AutoSim 1 , a high-fidelity simulation tool that models details about road networks, including individual lanes, lane markings, intersections, legal intersection traversability, etc.

“…Some of those postulates can be expanded to generic situations. Additional work preformed at the Sharif University of Technology of Tehran [6] is based on two assumptions: the maximum speed on a road segment and the potential danger that could be incurred by traversing this road segment. These assumptions and concepts allow the simulation program to create a rank of actions the vehicle can execute based on the danger that could be incurred.…”

Section: Introductionmentioning

confidence: 99%

PRIDE: a hierarchical, integrated prediction framework for autonomous on-road driving

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.

Madhavan

Kootbally

In this paper, we present PRIDE (Prediction In Dynamic Environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (Real-time Control System) architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. The primary contribution of this paper is the approach that is used to provide a more synergistic framework, in which the results from one prediction algorithm are used to strengthen/weaken the results of another prediction algorithm. In particular, we describe how the results of an estimation-theoretic short-term (ST) prediction algorithm are used to validate the results of a situation-based long-term (LT) prediction algorithm. We show the results of the long-term prediction both before and after the results of the short-term predictions are integrated, and demonstrate how the latter provide better predictions of vehicle future positions using the AutoSim simulation package.