Evidence for a fundamental property of steering

In this paper, a general and fundamental property of steering is demonstrated: It is shown that steering corrections generally follow bell-shaped profiles of steering rate. The finding is strongly related to what is already known about reaching movements. Also, a strong linear relationship was found between the maximum steering wheel rate and the steering wheel deflection, something that indicates a constant movement time for the correction. Furthermore, by closer examination of those corrections that cannot be described by a single bell-shaped rate profile, it was found that they typically can be described using two or, in some cases three or four, overlapping profiles, something which relates to superposition of motor primitives

Effects of Visual and Cognitive Distraction on Lane Change Test Performance

Driver errors related to visual and cognitive distraction were studied in the context of the Lane Change Test (LCT). New performance metrics were developed in order to capture the specific effects of visual and cognitive distraction. In line with previous research, it was found that the two types of distraction impaired driving in different ways. Visual, but not cognitive, distraction led to reduced path control. By contrast, only cognitive distraction affected detection and recognition/response selection. Theoretical and practical implications of these results are discussed

Effects of experience and electronic stability control on low friction collision avoidance in a truck driving simulator

Two experiments were carried out in a moving-base simulator, in which truck drivers of varying experience levels encountered a rear-end collision scenario on a low-friction road surface, with and without an electronic stability control (ESC) system. In the first experiment, the drivers experienced one instance of the rear-end scenario unexpectedly, and then several instances of a version of the scenario adapted for repeated collision avoidance. In the second experiment, the unexpected rear-end scenario concluded a stretch of driving otherwise unrelated to the study presented here. Across both experiments, novice drivers were found to collide more often than experienced drivers in the unexpected scenario. This result was found to be attributable mainly to longer steering reaction times of the novice drivers, possibly caused by lower expectancy for steering avoidance. The paradigm for repeated collision avoidance was able to reproduce the type of steering avoidance situation for which critical losses of control were observed in the unexpected scenario and, here, ESC was found to reliably reduce skidding and control loss. However, it remains unclear to what extent the results regarding ESC benefits in repeated avoidance are generalisable to unexpected situations. The approach of collecting data by appending one unexpected scenario to the end of an otherwise unrelated experiment was found useful, albeit with some caveats

A Review of Near-Collision Driver Behavior Models

Objective: This article provides a review of recent models of driver behavior in on-road collision situations. Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments.<p> Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios.<p> Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms.<p> Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended.<p> Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work

Drivers use active gaze to monitor waypoints during automated driving

Peer reviewe

Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study

The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the \emph{Commotions} model -- a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control -- for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles.Comment: 6 pages, 2 figure

An active inference model of car following: Advantages and applications

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets

Creating Kinematics-dependent Pedestrian Crossing Willingness Model When Interacting with Approaching Vehicle

The interaction between automated vehicles (AVs) and vulnerable road users is increasingly important since the adoption of AVs is closer to reality. Particularly, the pedestrians' crossing behaviour are extremely complex, and it is difficult for AVs to predict pedestrians' decisions and motion behaviour. One of the important problems is how to characterize pedestrians crossing willingness (PCW), which is important for AV systems. Currently, few models have been proposed to characterize PCW. The most relevant models, pedestrian gap acceptance models, are mostly pure statistical approaches which are difficult to apply to a wide range of scenarios. In this paper, to avoid these drawbacks, we developed a novel PCW model by employing a continuously changing psychophysical stimulus, looming, which characterizes the visual information of approaching vehicles through the kinematics model of crossing scenario. In addition, a perception threshold is introduced to constrain the model. Results in this study showed that the PCW model can accurately capture the effects of the vehicle speed, distance and size on pedestrians' behaviour pattern. It was also found that pedestrians have maximum willingness to cross the street when this stimulus is beyond the perception threshold. We found that the model fit well with data collected from previous gap acceptance studies