Abstract

dvanced Driver Assistance Systems (ADAS) are becoming ubiquitous, and gradually take over the role of human drivers in the vision of the automotive sector. Humans are different from most systems: while in general humans exhibit a much higher error rate when performing specific functions, they are also unmatched in their adaptability, and their ability to recognize patterns and anticipate on these. In this paper we derive temporal requirements on future ADAS operation, needed to at least match human driving behavior in dense traffic. We examine Adaptive Cruise Control and Automated Emergency Braking systems at highway speeds, derive temporal requirements, and show that in dense traffic situations intent communication has a significant benefit to improve systems operation. The resulting requirements will challenge ADAS developments in the coming years. cop. 2015 IEEE.

Abstract

Introduction: This paper describes the modification and development of methodologies to assess the impacts of Intelligent Transport Systems (ITS) applications for Vulnerable Road users (VRUs) in the domains of safety, mobility and comfort. This effort was carried out in the context of the VRUITS project whose aim was to produce results at the EU-28 level. Methods: An existing safety methodology was modified to take into account specific VRU aspects. The mobility and comfort assessments methodologies were developed in the project. Results: The safety, mobility and comfort methodologies were applied to ten ITS applications for VRUs. The first innovation determined how the nine safety mechanisms for ex-ante analysis of ITS applications, including direct and indirect effects, can incorporate the important characteristics of the VRU groups (pedestrians, cyclists and Power-Two-Wheeler riders) in the analysis. The second innovation developed a conceptual model for mobility and comfort. Thirdly, the estimation of quantitative effects, using literature, empirical findings and expert judgement, was developed. Conclusions: The new safety, mobility and comfort assessment methodologies were applied to calculate the respective effects for VRUs using ITS. These results are ex-ante findings, as very few to no empirical results for ITS applications for VRUs are available. In order to improve the accuracy of the estimates, there is a need for better standardized data and at the European level. Finally, validation of the methods could be done in the future field operational tests focusing on measuring user behavior. © 2017, The Author(s). Document type: Article

Abstract

n important question in automated driving research is how quickly drivers take over control of the vehicle in response to a critical event or a take-over request. Although a large number of studies have been performed, results vary strongly. In this study, we investigated mean take-over times from 129 studies with SAE level 2 automation or higher. We used three complementary approaches: (1) a within-study analysis, in which differences in mean take-over time were assessed for pairs of experimental conditions, (2) a between-study analysis, in which correlations between experimental conditions and mean take-over times were assessed, and (3) a linear mixed-effects model combining between-study and within-study effects. The three methods showed that a shorter mean take-over time is associated with a higher urgency of the situation, not using a handheld device, not performing a visual non-driving task, having experienced another take-over scenario before in the experiment, and receiving an auditory or vibrotactile take-over request as compared to a visual-only or no take-over request. A consistent effect of age was not observed. We also found the mean and standard deviation of the take-over time were highly correlated, indicating that the mean is predictive of variability. Our findings point to directions for new research, in particular concerning the distinction between drivers’ ability and motivation to take over, and the roles of urgency and prior experience. © 2019 Elsevier Ltd

Abstract

In conditionally automated driving, drivers do not have to monitor the road, whereas in partially automated driving, drivers have to monitor the road permanently. We evaluated a dynamic allocation of monitoring tasks to human and automation by providing a monitoring request (MR) before a possible take-over request (TOR), with the aim to better prepare drivers to take over safely and efficiently. In a simulator-based study, an MR + TOR condition was compared with a TOR-only condition using a within-subject design with 41 participants. In the MR + TOR condition, an MR was triggered 12 s before a zebra crossing, and a TOR was provided 7 s after the MR onset if pedestrians crossing the road were detected. In the TOR-only condition, a TOR was provided 5 s before the vehicle would collide with a pedestrian if the participant did not intervene. Participants were instructed to perform a self-paced visual-motor non-driving task during automated driving. Eye tracking results showed that participants in the MR + TOR condition responded to the MR by looking at the driving environment. They also exhibited better take-over performance, with a shorter response time to the TOR and a longer minimum time to collision as compared to the TOR-only condition. Subjective evaluations also showed advantages of the MR: participants reported lower workload, higher acceptance, and higher trust in the MR + TOR condition as compared to the TOR-only condition. Participants’ reliance on automation was tested in a third drive (MR-only condition), where automation failed to provide a TOR after an MR. The MR-only condition resulted in later responses (and errors of omission) as compared to the MR + TOR condition. It is concluded that MRs have the potential to increase safety and acceptance of automated driving as compared to systems that provide only TORs. Drivers’ trust calibration and reliance on automation need further investigation. © 2019 Elsevier Ltd

Abstract

Co-operative driving with speed adaptation functionality has great potential to improve traffic-throughput, traffic-safety, and environmental-impact on heavily used traffic-infrastructures. A driving-simulator study was performed to investigate the driver behaviour with respect to such driver-support systems (Zero-, Advisory-, Intervention-, and Controlling). This paper describes the results of one specific scenario, a cut-in scenario. The results show a small reaction time, which was smaller than the response-time required for stabilizing the manoeuvre. Subjective measures show that the experienced-effort for the Controlling system was the smallest, that the satisfaction for the Controlling system was the highest and that the usefulness of all systems was positive.