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.

/pdf/understanding-and-modeling-the-human-driver-4i18rkrqa9.pdf

Understanding and modeling the human driver.

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.

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

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.

https://toledo.net.technion.ac.il/files/2012/12/TomerToledoPhD.pdf

Integrated driving behavior modeling

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, ...

New Results in Driver Steering Control Models

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.

/pdf/autonomous-vehicles-disengagements-accidents-and-reaction-47rge5oso7.pdf

Autonomous Vehicles: Disengagements, Accidents and Reaction Times.

This study investigated the effects of load uncertainty on the lifting characteristics of 40 male volunteers during the initial portion of a lift. Twenty subjects were experienced weightlifters while another 20 were subjects who had never lifted weights nor held a job that required them to on a regular basis. The subjects each lifted a container 20 × 45 × 40 cm, with handles, from floor to waist height 12 times with loads of 68, 10·2 or 13·6 kg. The loads were lifted under conditions of either havingor not having verbal and visual knowledge of the load magnitude prior to the lift. The subjects were allowed to perform the lift in a manner of their choosing. A 2 (groups) × 3 (loads) × 2 (load knowledge) ANOVA was performed on the data. Maximim force (Fmax) value analysis revealed group and technique differences. The experienced lifters had lower stress levels at L4/L5 and utilized two technique strategies that were dependent upon the load knowledge condition, whereas the non-lifters used the same strategy f...

The effects of load knowledge on stresses at the lower back during lifting

Increased attention to carpal tunnel syndrome in industry has resulted in the development of several proposed screening tests. This investigation evaluated the use of two portable devices for measuring motor nerve conduction time and tactile sensitivity to 120 Hz vibration in a field setting. Forty-seven control participants, 63 manufacturing plant workers with and without symptoms of carpal tunnel syndrome, and 22 patients with physician-diagnosed carpal tunnel syndrome were tested with the NervePace electroneurometer and the Vibraton II vibration threshold measurement device. Nerve conduction time differed significantly between the controls, the asymptomatic workers, and the participants with carpal tunnel syndrome or symptoms of carpal tunnel syndrome. The vibration threshold was higher in the carpal tunnel syndrome group than in the other groups; however, further examination of the data revealed no differences in threshold unless nerve conduction time exceeded the control mean by at least three standard deviations. The false-negative rates associated with the tests limit their usefulness in screening for carpal tunnel syndrome.

Use of motor nerve conduction testing and vibration sensitivity testing as screening tools for carpal tunnel syndrome in industry

Four representative vehicles used in a previous open-loop comparative study were tested with a sampling of volunteers from the general public. The tests included pre-planned and simulated emergency maneuvers. Various vehicle input and response parameters were measured, and the results were used to evaluate current open-loop test maneuvers and parameters. In addition, the degree to which this sampling of drivers utilized the full capabilities of the vehicles was determined. Drivers rarely use the full capabilities of vehicles and tend to adjust their inputs to neutralize differences among vehicles.

Driver Inputs during Emergency or Extreme Vehicle Maneuvers

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 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. A new model for determining stopping sight distance requirements for geometric design of highways is presented. This 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 e...

New Stopping Sight Distance Model for Use in Highway Geometric Design

A simple, reliable instrumentation package with an on-board computer for installation in a test vehicle or the test driver's own vehicle is described. This package was used in a research project recently completed, an empirical investigation of stopping sight distance. Selected data on perception-response time (PRT) and braking performance under artificial and simulated on-the-road emergency conditions are presented. PRTs were less than the AASHTO assumed constant of 2.5 sec even at the 95th percentile. Braking performance in terms of steady deceleration was greater than -0.32 g at the 95th percentile.

Rodger J. Koppa

Papers

The effects of load knowledge on stresses at the lower back during lifting

Use of motor nerve conduction testing and vibration sensitivity testing as screening tools for carpal tunnel syndrome in industry

Driver Inputs during Emergency or Extreme Vehicle Maneuvers

New Stopping Sight Distance Model for Use in Highway Geometric Design

Measuring Driver Performance in Braking Maneuvers