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- Bianchi Piccinini, Giulio, 1982, et al.
(författare)
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How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models
- 2020
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Ingår i: Human Factors. - Chalmers University of Technology, Gothenburg, Sweden.; Volvo Group Trucks Technology, Gothenburg, Sweden.; Virginia Tech Transportation Institute, Blacksburg, USA.; University of Leeds, UK.; VTI, Gothenburg, Sweden. : SAGE Publications. - 1547-8181 .- 0018-7208. ; 62:7, s. 1212-1229
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Tidskriftsartikel (refereegranskat)abstract
- Objective: This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.
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- Bärgman, Jonas, 1972, et al.
(författare)
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The UDrive dataset and key analysis results
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- UDrive is a large European naturalistic driving study, sponsored by the European Commission (FP7).Nineteen partners across Europe have come together and, along with stakeholders, defined researchquestions, developed data acquisition, collected and managed data, and finally, performed a first analysis onthe UDrive dataset with respect to driver/rider behaviour related to traffic safety and the environment (ecodriving).This document presents key results of the UDrive analysis performed in UDrive Sub-project 4: Data analysis.It also describes the UDrive dataset and, in brief, how we got here.
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- Kovaceva, Jordanka, 1980, et al.
(författare)
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Car drivers overtaking cyclists: A European perspective using naturalistic driving data
- 2017
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Ingår i: The 6th International Naturalistic Driving Research Symposium, the Hague, the Netherlands, 8-9 June 2017.
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Konferensbidrag (refereegranskat)abstract
- In Europe, the number of road crashes is decreasing, while the number of crashes involving cyclists are not decreasing at the same rate as car crashes. Crashes which occur while the vehicle is overtaking a cyclist often result in severe injuries or fatalities. Understanding the behaviour of car drivers overtaking cyclists, can facilitate increased road safety through improved guidelines and policies, as well as in-vehicle technologies.This study investigates how car drivers overtake cyclists on rural roads in four European countries by analysing the UDrive naturalistic driving data. One objective is to understand if, in different countries, there is a difference in the lateral distance when the car is passing the cyclist. Other objective is to investigate if the time-to-collision (TTC), at the start of the overtaking, affects the lateral distance when the car is passing the cyclist.Minor differences between countries were found with respect to lateral distance. Greater lateral distance while passing cyclists was observed with increase in time-to-collision at the start of the overtaking. Furthermore, vehicle speed, distance between the lane edge and the cyclists, and the presence of leading vehicle significantly affected the driver comfort zone. The driver comfort zone during overtaking manoeuvres from naturalistic driving data could provide information for legislators and policy makers in Europe, as well as support safety system design in the automotive industry.
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