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Driving behavior can be influenced by many factors that are not feasible to collect in driving behavior studies. The research presented in this paper investigates the characteristics of a wide range of driving behaviors linking driving states to the drivers' actions. The proposed methodology is structured such that a known state can be linked to multiple(More)
This paper proposes a rule-based neural networkmodel to simulate driver behavior in terms of longitudinal and lateral actions in two driving situations, namely car-following situation and safety critical events. A fuzzy rule based neural network is constructed to obtain driver individual driving rules from their vehicle trajectory data. A machine learning(More)
Online presence of information and services is pervasive. Teaching and learning are no exception. Courseware management systems play an important role in enhancing instructional delivery for either traditional day, full-time students or non-traditional evening, party-time adult learners enrolled in online programs. While online course management tools are(More)
This research effort aims to investigate the hypothesis that drivers apply different driving styles in their daily driving tasks. A two-step algorithm is used for segmentation and clustering. First, a car-following period is broken into different duration segments that account for their temporal distribution. Second, the segments produced by the previous(More)
Heterogeneous driver behavior during safety-critical events is more complicated than normal driving situations and is difficult to capture by statistical models. This paper applies an agent-based reinforcement learning method to represent heterogeneous driving behavior for different drivers during safety-critical events. The naturalistic driving data of(More)
This paper presents a research effort aimed at modeling normal and safety-critical driving behavior in traffic under naturalistic driving data using agent based modeling techniques. Neuro-fuzzy reinforcement learning was used to train the agents. The developed agents were implemented in the VISSIM simulation platform and were evaluated by comparing the(More)
This research effort describes a methodology to investigate the effects of personality and emotion on driver behavior. The methodology detailed in this paper has four main components: development of psychological personality surveys, development of psychological emotion surveys, physiological data collection, and development of simulator scenarios. There(More)
An agent-based multi-layer reinforcement learning (RL) framework for naturalistic driving behavior simulation in traffic is introduced. Each agent is a replication of an individual driver. Each agent is implemented by applying artificial intelligence concepts, including: fuzzy logic, neural networks, and reinforcement learning algorithms. A revised(More)
An agent-based, artificial intelligence technique known as reinforcement learning has been used to capture vehicle behavior and simulate driver's actions in traffic, especially during emergency situations. This paper discusses the training parameters and their influence on agent simulation performance. A type of agent training technique called Neuro-Fuzzy(More)