David A. McAllester

ICRA Conference 2011 Conference Paper

Adaptive non-planar road detection and tracking in challenging environments using segmentation-based Markov Random Field

• Chunzhao Guo
• Seiichi Mita
• David A. McAllester

Many roads made for land vehicles are not totally planar and present uphill and downhill slopes that follow the environment topography. Moreover, the road appearance is often affected by a number of factors in challenging conditions. In this paper, we present an adaptive non-planar road detection and tracking approach which overcomes these difficulties by a piecewise planar road model as well as a Markov Random Field (MRF)-based alternating optimization using belief propagation (BP) on segmented images and a hard conditional Expectation Maximization (EM) algorithm to achieve adaptability and optimality. The proposed framework incorporates image evidence, geometry information, and temporal support such that the graph we build and the well-defined energy minimization formulation can exploit the essence of the roads that is invariant in challenging environments. Experimental results in various real challenging traffic scenes show the effectiveness of the proposed approach.

ICML Conference 2011 Conference Paper

Convex Max-Product over Compact Sets for Protein Folding

• Jian Peng 0001
• Tamir Hazan
• David A. McAllester
• Raquel Urtasun

IROS Conference 2011 Conference Paper

Vehicle detection and tracking at nighttime for urban autonomous driving

• Hossein Tehrani Niknejad
• Koji Takahashi
• Seiichi Mita
• David A. McAllester

This paper proposes a method for on road detecting and tracking of multi vehicles at nighttime in urban environment. The features of vehicles including root and part filters are learned as a weighted deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). Detected vehicles are tracked through a particle filter which estimates near optimum likelihoods by calculating the maximum HOG features compatibility for both root and parts of the tracked vehicles. Tracking likelihoods are iteratively used as a priori probability to generate vehicle hypothesis regions. Extensive experiments with close range IR camera in urban scenarios showed that the efficiency of the proposed method for detecting and tracking of multi vehicles at night time.

ICML Conference 2010 Conference Paper

Discriminative Latent Variable Models for Object Detection

• Pedro F. Felzenszwalb
• Ross B. Girshick
• David A. McAllester
• Deva Ramanan

IROS Conference 2010 Conference Paper

Lane detection and tracking in challenging environments based on a weighted graph and integrated cues

• Chunzhao Guo
• Seiichi Mita
• David A. McAllester

Real-time lane detection and localization is one of the key issues for many intelligent transportation systems. In this paper, we present a lane detection and tracking approach designed to work in challenging environments where lane boundaries may be low-contrast and changeful with noise due to a number of factors such as wear, type, lighting and weather conditions, etc. In the method, a sophisticated cascade lane feature detector is applied to cope with challenging environments at the very beginning of the detection and a weighted graph is subsequently constructed by integrating intensity as well as geometry cues, reflecting the confidence of each pixel as a lane feature. In order to deal with complex road geometry, we employ Catmull-Rom splines to represent lane boundaries and the left and right lane boundaries are estimated separately in a tracking process using particle filter based on the weighted graph. In the proposed framework, unlike most of previous methods we lay a strong emphasis on accurate and effective lane feature detection since the challenges happen in the very first step of lane detection, and accurately detected lane features can be expected to reduce the complexity and difficulty, as well as improve the accuracy of lane detection in the following steps.

IROS Conference 2009 Conference Paper

Stereovision-based road boundary detection for intelligent vehicles in challenging scenarios

• Chunzhao Guo
• Seiichi Mita
• David A. McAllester

Road detection is a crucial problem for intelligent vehicles and mobile robots. Most of the methods proposed nowadays only achieve reliable results in relatively well-arranged environments. In this paper, we proposed a stereovision-based road boundary detection method by combining homography estimation and MRF-based belief propagation to cope with challenging scenarios such as unstructured roads with unhomogeneous surfaces. In the method, each pixel in the reference image is firstly labeled as ¿road¿ or ¿non-road¿ by minimizing a well defined energy function that accounts for the planar road region. Subsequently, both of the road boundaries are generated using Catmull-Rom splines based on RANdom SAmple Consensus (RANSAC) algorithm with varying road structure models to help the intelligent vehicle understand the structure as well as safe range of current road. In the suggested framework, both intensity and geometry information of road scenarios are used to contain all the regions belonging to the planar road plane, and the left and right road boundaries are generated separately using a robust fitting algorithm to handle different road structures. Therefore, more accurate as well as robust detection of the road can be expected. Experimental results on a wide variety of typical but challenging scenarios have demonstrated the effectiveness of the proposed method.

UAI Conference 2004 Conference Paper

Case-Factor Diagrams for Structured Probabilistic Modeling

• David A. McAllester
• Michael Collins 0001
• Fernando C. N. Pereira

We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Boolean formulas that we call case-factor diagrams (CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for circuits of bounded tree width (unlike BDDs) and can concisely represent the set of parse trees over a given string undera given context free grammar (also unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of Boolean assignments and a weight (or cost) for each individual Boolean variable. We give an insideoutside algorithm for simultaneously computing the marginal of each Boolean variable, and a Viterbi algorithm for finding the mininum cost variable assignment. Both algorithms run in time proportional to the size of the CFD.

ICML Conference 2002 Conference Paper

Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation

• Robert E. Schapire
• Peter Stone 0001
• David A. McAllester
• Michael L. Littman
• János A. Csirik

UAI Conference 1999 Conference Paper

Approximate Planning for Factored POMDPs using Belief State Simplification

• David A. McAllester
• Satinder Singh 0001

We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy-efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.

AIJ Journal 1992 Journal Article

Natural language syntax and first-order inference

• David A. McAllester
• Robert Givan

We have argued elsewhere that first-order inference can be made more efficient by using nonstandard syntax for first-order logic. In this paper we define a syntax for first-order logic based on the structure of natural language under Montague semantics. We show that, for a certain fairly expressive fragment of this language, satisfiability is polynomial time decidable. The polynomial time decision procedure can be used as a subroutine in general purpose inference systems and seems to be more powerful than analogous procedures based on either classical or taxonomic syntax.

FOCS Conference 1988 Conference Paper

Nonexpressibility of Fairness and Signaling

• David A. McAllester
• Prakash Panangaden
• Vasant Shanbhogue

Expressiveness results for indeterminate data flow primitives are established. Choice primitives with three differing fairness assumptions are considered, and it is shown that they are strictly inequivalent in expressive power. It is also shown that the ability to announce choices enhances the expressive power of two of the primitives. These results are proved using a very crude semantics and will thus apply in any reasonable theory of process equivalence. >