Detection of Collision Events by Older and Younger Drivers

Recently (Andersen et al., 2000; 1998) we found that older drivers had poorer performance than younger drivers at detecting an impending collision during braking. In the present study we examined whether older drivers have poorer performance than younger drivers at detecting a collision with a moving object. 22 older and younger drivers were presented with computer generated scenes of a roadway in a driving simulator. Located in the scene was a single object that moved independently of the vehicle motion and that was or was not on a collision path with the vehicle. Overall older drivers were less sensitive to detect a collision than younger drivers, with performance worse for long as compared to short time to contact (TTC) conditions

Car Following by Optical Parameters

A model for car following based solely on optical parameters was developed and compared with performance of human drivers in a simulator. The model uses the optical size of the back of the car being followed and the first derivative of its optical size as inputs. The model consists of two components: one that accelerates to maintain the visual size of the leading car, and another that accelerates to minimize changes in the rate of change of the visual size of the leading car. The simulator presented drivers with a leading car that was changing its velocity according to a sum of non-harmonic sines. Comparisons of human drivers’ performance with the models’ show a high degree of similarit

Detection and Avoidance of Collisions: the REACT Model

An important perceptual task during driving is the ability to detect and avoid collisions. Failure to accurately perform this task can have serious consequences for the driver and passengers. The present research developed and tested a model of car following by human drivers, as part of a general model under development of a human driver. Unlike other car following models that are based on 3D parameters (e.g., range or distance) the present model is based on the visual information available to the driver. The model uses visual angle and change in visual angle to regulate speed during car following. Human factors experiments in a driving simulator examined performance in car following to speed variations defined by sine wave oscillations in speed, sum of sine wave oscillations, and ramp function. In addition, using real world driving data the model was applied to 6 driving events. The model provided a good fit to car following performance in the driving simulation studies as well as the real-world driving data, accounting for up to 96% of the variability in speed for the real world driving events

Detection and Avoidance of Collisions: the REACT Model

An important perceptual task during driving is the ability to detect and avoid collisions. Failure to accurately perform this task can have serious consequences for the driver and passengers. The present research developed and tested a model of car following by human drivers, as part of a general model under development of a human driver. Unlike other car following models that are based on 3D parameters (e.g., range or distance) the present model is based on the visual information available to the driver. The model uses visual angle and change in visual angle to regulate speed during car following. Human factors experiments in a driving simulator examined performance in car following to speed variations defined by sine wave oscillations in speed, sum of sine wave oscillations, and ramp function. In addition, using real world driving data the model was applied to 6 driving events. The model provided a good fit to car following performance in the driving simulation studies as well as the real-world driving data, accounting for up to 96% of the variability in speed for the real world driving events.