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We present an autonomous driving research vehicle with minimal appearance modifications that is capable of a wide range of autonomous and intelligent behaviors, including smooth and comfortable trajectory generation and following; lane keeping and lane changing; intersection handling with or without V2I and V2V; and pedestrian, bicyclist, and workzone(More)
We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous(More)
“Socially cooperative driving” is an integral part of our everyday driving, hence requiring special attention to imbue the autonomous driving with a more natural driving behavior. In this paper, an intention-integrated Prediction- and Cost function-Based algorithm (iPCB) framework is proposed to enable an autonomous vehicle to perform(More)
In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low(More)
In this paper, a prediction- and cost function-based algorithm (PCB) is proposed to implement robust freeway driving in autonomous vehicles. A prediction engine is built to predict the future microscopic traffic scenarios. With the help of a human-understandable and representative cost function library, the predicted traffic scenarios are evaluated and the(More)
In this paper, we propose a novel planning framework that can greatly improve the level of intelligence and driving quality of autonomous vehicles. A reference planning layer first generates kinematically and dynamically feasible paths assuming no obstacles on the road, then a behavioral planning layer takes static and dynamic obstacles into account.(More)
In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle decision making is modeled as a Markov Decision Process (MDP), then extended to a QMDP framework. Based on MDP/QMDP, three kinds of uncertainties are taken into account: sensor(More)
This paper introduces a robust prediction- and cost-function based algorithm for autonomous freeway driving. A prediction engine is built so that the autonomous vehicle is able to estimate human drivers' intentions. A cost function library is used to help behavior planners generate the best strategies. Finally, the algorithm is tested in a real-time vehicle(More)
We propose a robust object tracking algorithm for distance keeping. Taking advantage of a context-based region of interest, we are able to maximize the performance of each sensor, and reduce the computation time since we only focus on the targets inside the region. Tracking targets in road coordinates enables finding the distance-keeping target on any(More)