Rodger J. Koppa

Bio: Rodger J. Koppa is an academic researcher from Texas A&M University. The author has contributed to research in topics: Stopping sight distance & Poison control. The author has an hindex of 12, co-authored 29 publications receiving 546 citations. Previous affiliations of Rodger J. Koppa include Texas A&M University System & Texas A&M Transportation Institute.

Determination of stopping sight distances

Abstract: This report describes the development of recommended revisions to the stopping sight distance (SSD) design policy that appears in portions of Chapters II and III of the 1994 American Association of State Highway and Transportation Officials (AASHTO) publication, "A Policy on Geometric Design of Highways and Streets" (referred to as the Green Book). It also proposes modifications to other sections of the Green Book that currently reference stopping sight distance. The contents of this report are, therefore, of immediate interest to highway designers; highway operations, capacity, and traffic control personnel; and others concerned with highway safety. The report's conclusions are derived from field observations of driver performance, driver visual capacity, driver eye heights, and vehicle heights, as well as safety and operational studies.

Driver Perception-Brake Response in Stopping Sight Distance Situations

Abstract: One of the most important requirements in highway design is the provision of adequate stopping sight distance at every point along the roadway. At a minimum, this sight distance should be long enough to enable a vehicle traveling at or near the design speed to stop before reaching a stationary object in its path. Stopping sight distance is the sum of two components—brake reaction distance and braking distance. Brake reaction distance is based on the vehicle's speed and the driver's perception-brake reaction time (PBRT). Four separate, but coordinated, driver braking performance studies measured driver perception—brake response to several different stopping sight distance situations. The results from the driver braking performance studies suggest that the mean perception-brake response time to an unexpected object scenario under controlled and open road conditions is about 1.1 s. The 95th percentile perception-brake response times for these same conditions was 2.0 s. The findings from these studies are con...

Driver Braking Performance in Stopping Sight Distance Situations

Abstract: Assumed driver braking performance in emergency situations is not consistent in the published literature. A 1955 study stated that in an emergency situation "it is suspected that drivers apply their brakes as hard as possible." This idea differs from a 1984 report that states drivers will "modulate" their braking to maintain directional control. Thus, additional information is needed about driver braking performance when an unexpected object is in the roadway. In this research driver braking distances and decelerations to both unexpected and anticipated stops were measured. The study design allowed for differences in vehicle handling and driver capabilities associated with antilock braking systems (ABS), wet and dry pavement conditions, and the effects of roadway geometry. Vehicle speeds, braking distances, and deceleration profiles were determined for each braking maneuver. The research results show that ABS result in shorter braking distances by as much as 30 m at 90 km/h. These differences were most no...

Effects of Horizontal Curvature on Driver Visual Demand

Abstract: A consistent design allows drivers to perform safely the task of driving, allowing attention or capacity to be dedicated to obstacle avoidance and navigation. A measure of the consistency of a design is the amount of visual information needed by a driver to maintain an acceptable path on the roadway. Vision occlusion is a technique that measures driver visual demand on a roadway. It allows a more direct evaluation of the effects of various geometric elements on the driver. Studies of the effects of variations of curve radius, deflection angle, spacing, and sequences revealed several relationships between roadway geometry and visual demand. Curve radius and its reciprocal were found to be significantly related to visual demand in both on-road and test track studies. Small changes in visual demand were also found between types of curve pairs (S and broken back) with differing spacing between the curves. Visual demand was found to be a promising measure of effectiveness for use in studies of design consistency.

A new stopping sight distance model for use in highway geometric design

Abstract: Stopping sight distance is an important design parameter in that it defines the minimum sight distance that must be provided at all points along the highway. Thus, it influences geometric design values, construction costs, and highway safety. Stopping sight distance is defined as the sum of two components - brake reaction distance and the braking distance. The basic model for calculating stopping sight distances was formalized in 1940, and the model's parameters have been altered to compensate for changes in eye height, object height, and driver behavior over the past 50 years. Recent studies, however, question whether the model's parameters and assumptions represent real-world conditions. This paper presents a new model for determining stopping sight distance requirements for geometric design of highways. The new model is based on parameters describing driver and vehicle capabilities that can be validated with field data and defended as safe driving behavior. More than 50 drivers, 3,000 braking maneuvers, 1,000 driver eye heights, and 1,000 accident narratives were used in developing the recommended parameter values for the new model. The recommended values are attainable by most drivers, vehicles, and roadways. The new model results in stopping sight distances, sag vertical curve lengths, and lateral clearances that are between the current minimum and desirable requirements, and crest vertical curve lengths that are shorter than current minimum requirements.

Understanding and modeling the human driver.

Abstract: Summary This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.

Prediction of the expected safety performance of rural two-lane highways

Abstract: This report presents an algorithm for predicting the safety performance of a rural two-lane highway. The accident prediction algorithm consists of base models and accident modification factors for both roadway segments and at-grade intersections on rural two-lane highways. The base models provide an estimate of the safety performance of a roadway or intersection for a set of assumed nominal or base conditions. The accident modification factors adjust the base model predictions to account for the effects on safety for roadway segments of lane width, shoulder width, shoulder type, horizontal curves, grades, driveway density, two-way left-turn lanes, passing lanes, roadside design and the effects on safety for at-grade intersections of skew angle, traffic control, exclusive left- and right-turn lanes, sight distance, and driveways. The accident prediction algorithm is intended for application by highway agencies to estimate the safety performance of an existing or proposed roadway. The algorithm can be used to compare the anticipated safety performance of two or more geometric alternatives for a proposed highway improvement. The accident prediction algorithm includes a calibration procedure that can be used to adapt the predicted results to the safety conditions encountered by any particular highway agency on rural two-lane highways. The algorithm also includes an Empirical Bayes procedure that can be applied to utilize the safety predictions provided by the algorithm together with actual site-specific accident history data.

Integrated driving behavior modeling

Abstract: This paper develops, implements and tests a framework for driving behavior modeling that integrates the various decisions, such as acceleration, lane changing and gap acceptance. Furthermore, the proposed framework is based on the concepts of short-term goal and short-term plan. Drivers are assumed to conceive and perform short-term plans in order to accomplish short-term goals. This behavioral framework supports a more realistic representation of the driving task, since it captures drivers' planning capabilities and allows decisions to be based on anticipated future conditions. An integrated driving behavior model, which utilizes these concepts, is developed. The model captures both lane changing and acceleration behaviors. The driver's short-term goal is defined by the target lane. Drivers who wish to change lanes but cannot change lanes immediately, select a short-term plan to perform the desired lane change. Short-term plans are defined by the various gaps in traffic in the target lane. Drivers adapt their acceleration behavior to facilitate the lane change using the target gap. Hence, inter-dependencies between lane changing and acceleration behaviors are captured.

New Results in Driver Steering Control Models

Abstract: The dynamic control properties of drivers and driver/vehicle systems in steering operations have been widely investigated. This paper presents a short review of the combined compensatory, pursuit, ...

Autonomous Vehicles: Disengagements, Accidents and Reaction Times.

Abstract: Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.