Omar Ahmad

Hierarchical, concurrent state machines for behavior modeling and scenario control

This paper presents a framework for behavior modeling and scenario control based on hierarchical, concurrent state machines (HCSM). We present the structure and informal operational semantics of hierarchical, concurrent state machines. We describe the use of HCSM for scenario control in the Iowa Driving Simulator (IDS), a virtual environment for real-time driving simulation. The paper concludes with an outline of a forthcoming HCSM-based scenario authoring system that will permit non-specialists to graphically program behaviors and design experiments for IDS.<<ETX>>

Design of Simulator Scenarios to Study Effectiveness of Electronic Stability Control Systems

The mission of the National Advanced Driving Simulator is to conduct highway safety research that will reduce annual loss of life on U.S. roadways. The simulator is well suited in its ability to replicate vehicle dynamics—and associated motion and visual cues—realistically to conduct complex experiments. It is unique in its ability to study vehicle control and loss-of-control situations in a safe and controlled environment. These capabilities make it an appropriate device to study the effectiveness of electronic stability control (ESC) systems, in which proper handling during loss of vehicle control is critical to assess system efficacy. The focus of the study is on challenges associated with creating repeatable yet unexpected scenario events in which loss of control is imminent for most drivers. Scenario events designed for a large-scale experiment to study ESC systems are detailed, data derived from these scenarios are discussed, and findings of scenario effectiveness are presented. A discussion of what...

Matching Simulator Characteristics to Highway Design Problems

Driving simulators hold much promise for addressing roadway design issues. However, although simulators have demonstrated their value in experimental research addressing driver performance, their ability to support road design projects has not been as clearly established. This paper describes a design-centered framework to make simulators valuable for traffic engineers and geometric designers. This framework includes several steps: (a) identification of design issues that would benefit from driving simulators, (b) identification of simulator characteristics to match them to design issues, and (c) translation of driver performance data from the simulator to traffic behavior on the road. Several critical obstacles inhibit application of simulators to highway design. First, driving safety researchers and engineers comprise separate communities and their perspectives on how simulators can be applied to address road design issues often diverge. This paper seeks to reduce this divergence and make simulators useful to highway engineers. Interviews with engineers revealed important issues that simulators could address, such as intersection and interchange design. Second, driving simulators are often broadly defined as high fidelity, which provides little value in matching simulators to design issues. A survey of simulators and simulator characteristics clarifies the meaning of simulator fidelity and links it to road design issues. Third, simulators often produce data that do not correspond to data collected by traffic engineers. This mismatch can result from inadequate simulator fidelity, but can also arise from more fundamental sources—traffic engineers focus on traffic behavior and driving simulator researchers focus on driver behavior. Obstacles in using simulators for highway design reflect both technical and communication challenges.

Drivers’ Behavior Through a Yellow Light Effects of Distraction and Age

The decision to proceed through an intersection once the traffic signal has changed to yellow may increase the risk for a collision. This study sought to understand how age differences, cell phone use, and time to the stop line affected the likelihood that a driver would continue through a yellow light as observed in a driving simulator study. Four age groups were examined; novice (16 years), younger (18-25 years), middle (30-45 years) and older (50-60 years). The novice drivers were also between four to eight weeks of licensure. The likelihood that a driver would either drive through a yellow phase or stop was examined using a logistic regression model. A significant interaction effect between age groups and cell phone use was observed. More specifically, novice drivers talking on a handheld phone were significantly more likely than middle-aged drivers to proceed through the intersection. This study examines the safety consequences that may result for these novice drivers as they engage in distracting tasks.

Creating Pedestrian Crash Scenarios in a Driving Simulator Environment

Objective: In 2012 in the United States, pedestrian injuries accounted for 3.3% of all traffic injuries but, disproportionately, pedestrian fatalities accounted for roughly 14% of traffic-related deaths (NHTSA 2014). In many other countries, pedestrians make up more than 50% of those injured and killed in crashes. This research project examined driver response to crash-imminent situations involving pedestrians in a high-fidelity, full-motion driving simulator. This article presents a scenario development method and discusses experimental design and control issues in conducting pedestrian crash research in a simulation environment. Driving simulators offer a safe environment in which to test driver response and offer the advantage of having virtual pedestrian models that move realistically, unlike test track studies, which by nature must use pedestrian dummies on some moving track. Methods: An analysis of pedestrian crash trajectories, speeds, roadside features, and pedestrian behavior was used to create 18 unique crash scenarios representative of the most frequent and most costly crash types. For the study reported here, we only considered scenarios where the car is traveling straight because these represent the majority of fatalities. We manipulated driver expectation of a pedestrian both by presenting intersection and mid-block crossing as well as by using features in the scene to direct the drivers visual attention toward or away from the crossing pedestrian. Three visual environments for the scenarios were used to provide a variety of roadside environments and speed: a 20–30 mph residential area, a 55 mph rural undivided highway, and a 40 mph urban area. Results: Many variables of crash situations were considered in selecting and developing the scenarios, including vehicle and pedestrian movements; roadway and roadside features; environmental conditions; and characteristics of the pedestrian, driver, and vehicle. The driving simulator scenarios were subjected to iterative testing to adjust time to arrival triggers for the pedestrian actions. This article discusses the rationale behind creating the simulator scenarios and some of the procedural considerations for conducting this type of research. Conclusions: Crash analyses can be used to construct test scenarios for driver behavior evaluations using driving simulators. By considering trajectories, roadway, and environmental conditions of real-world crashes, representative virtual scenarios can serve as safe test beds for advanced driver assistance systems. The results of such research can be used to inform pedestrian crash avoidance/mitigation systems by identifying driver error, driver response time, and driver response choice (i.e., steering vs. braking).

ADAPTIVE CONTROLLERS FOR VEHICLE VELOCITY CONTROL FOR MICROSCOPIC TRAFFIC SIMULATION MODELS

This paper provides a description of a hybrid follow controller designed to automatically regulate the speed of controlled vehicles in car-following situations within a virtual environment of a microscopic traffic simulation system. In prior follow type algorithms the modeling goal was the correlation of aggregate traffic behavior; however, the main aim of this model is to appear realistic when viewed by a human participant driving a vehicle within the virtual environment of a driving simulator. The overall simulation system is briefly described along with specific problems that were addressed in this particular application. Examples of the models performance are given for typical driving situations.