Tim Allan Wheeler

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The validity of any traffic simulation model depends on its ability to generate representative driver acceleration profiles. This paper studies the effectiveness of recurrent neural networks in predicting the acceleration distributions for car following on highways. The long short-term memory recurrent networks are trained and used to propagate the(More)
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic(More)
Probabilistic microscopic traffic models provide a statistical representation of interactive behavior between traffic participants. They are crucial for the validation of automotive safety systems that make decisions based on surrounding traffic. The construction of such models by hand is error-prone and difficult to extend to the complete diversity of(More)
The accurate simulation and prediction of human behavior is critical in many transportation applications, including safety and energy management systems. Construction of human driving models by hand is time-consuming and error-prone, and small modeling inaccuracies can have a significant impact on the estimated performance of a candidate system. This paper(More)
POMDPs.jl is an open-source framework for solving Markov decision processes (MDPs) and partially observable MDPs (POMDPs). POMDPs.jl allows users to specify sequential decision making problems with minimal effort without sacrificing the expressive nature of POMDPs, making this framework viable for both educational and research purposes. It is written in the(More)
Validation of automotive safety systems can be done by simulating millions of driving traces. It is important that the distribution of initial scenes for these driving traces be as representative of reality as possible so that safety risk can be estimated accurately. This paper presents a methodology for constructing probability distributions over initial(More)
Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. Detailed radar simulations based on physical principles exist but are computationally intractable for(More)
Real data often contains a mixture of discrete and continuous variables, but many Bayesian network structure learning and inference algorithms assume all random variables are discrete. Continuous variables are often discretized, but the choice of discretization policy has significant impact on the accuracy, speed, and interpretability of the resulting(More)
Effective navigation of urban environments is a primary challenge remaining in the development of autonomous vehicles. Intersections come in many shapes and forms, making it difficult to find features and models that generalize across intersection types. New and traditional features are used to train several intersection intention models on real-world(More)
Automated driving requires designing a system capable of maintaining safety while simultaneously maintaining passenger comfort. Models of highway driving have high dimensionality and stochasticity traditionally specifying the histories for a large, varying number of agents in a continuous state and action space. Traditional value-based reinforcement(More)