John M. Dolan

Learn More
Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, M. N. Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matt McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Bryan(More)
We present a motion planner for autonomous highway driving that adapts the state lattice framework pioneered for planetary rover navigation to the structured environment of public roadways. The main contribution of this paper is a search space representation that allows the search algorithm to systematically and efficiently explore both spatial and temporal(More)
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)
To focus on the research issues surrounding collaborative behavior in multiple mobile-robotic systems, a great amount of low-level infrastructure is required. To facilitate our on-going research into multi-robot systems, we have developed RAVE, a software framework that provides a Real And Virtual Environment for running and managing multiple heterogeneous(More)
Recent research in robot exploration and mapping has focused on sampling hotspot fields, which often arise in environmental and ecological sensing applications. Such a hotspot field is characterized by continuous, positively skewed, spatially correlated measurements with the hotspots exhibiting extreme measurements and much higher spatial variability than(More)
An approach for formally verifying the safety of automated vehicles is proposed. Due to the uniqueness of each traffic situation, we verify safety online, i.e., during the operation of the vehicle. The verification is performed by predicting the set of all possible occupancies of the automated vehicle and other traffic participants on the road. In order to(More)
A key problem of robotic environmental sensing and moni-<lb>toring is that of active sensing: How can a team of robots<lb>plan the most informative observation paths to minimize<lb>the uncertainty in modeling and predicting an environmen-<lb>tal phenomenon? This paper presents two principled ap-<lb>proaches to efficient information-theoretic path planning(More)
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (DFAS) algorithm for mobile sensors to actively explore the road network to gather and(More)
On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework.(More)