Mikael Ljung Aust

Development and Application of an Integrated Evaluation Framework for Preventive Safety Applications

Preventive safety functions help drivers avoid or mitigate accidents. No quantitative methods have been available to evaluate the safety impact of these systems. This paper describes a framework for the assessment of preventive and active safety functions, which integrates procedures for technical performance, human factors, and safety assessments in one holistic approach. The concept of situational control , which is defined as the degree of control that a joint driver-vehicle system exerts over a traffic situation, has been introduced. Assessment involves technical evaluation , which assesses that the system and all subsystems work according to the functional and technical specifications, human factors evaluation, which assesses the behavioral situational control parameters, and safety potential evaluation, which performs a detailed assessment of internal and external impact mechanisms that change the situational control at the traffic level. The methodology has been applied to the INSAFES project, which integrates lateral and longitudinal control functions.

An integrated evaluation framework for preventive safety applications

PReVAL is a subproject of the PReVENT integrated project, in which different preventive safety functions have been developed and demonstrated. The major aims of the PReVAL project are to assess the safety potential of these functions and to develop a harmonized framework for the assessment of preventive safety applications and advanced driver assistance functions. The framework builds on the experiences gained in the PReVENT project and other related projects, such as AIDE, APROSYS, and eIMPACT. The framework integrates different procedures for technical and human factors evaluation and safety potential assessment in one holistic approach.

Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel

Objective: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. Background: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature. Method: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. Results: Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). Conclusion: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. Application: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.