Drivers’ response in an intersection scenario during manual and automated driving
Examensarbete för masterexamen
The role of automation is becoming increasingly important in the design of modern vehicles. This is reflected by a large development of advanced driver assistance systems, which are ever more affecting driving behaviour. This work is focused on the analysis and the modelling of driver’s response to a critical T-intersection scenario, considering different levels of automation. In this scenario, the subject vehicle is travelling on the main road, and a turning car coming from the perpendicular leg of the T-intersection, cuts in the subject vehicle path. The analysed data was collected during an experiment performed with a moving based driving simulator located at the Swedish National Road and Transport Research Institute (VTI) in Linköping (Sweden). The participants drove in four different experimental conditions: manual driving, intentional car following and two semi-automated driving conditions concerning the use of the adaptive cruise control and the traffic jam assist. The findings of this thesis showed that the main response to the turning car entrance was braking. The steering reaction was negligible since only small and occasional corrective steering were found. In this scenario, drivers reacted with a brake reaction time independently from the driving condition. However, a significant correlation between the brake reaction time and the time to intersection in the moment the turning car enters was found during manual driving, resulting in faster drivers’ reaction to an increase of risk. The braking behaviour was analysed taking into account the maximum deceleration reached, the jerk at the brake onset and the mean brake pedal force. The results of our analysis indicate that drivers react with a higher mean brake force in semi-automated driving modes comparing to manual driving. Consequently, this results in higher values for both brake jerk and maximum deceleration. Additionally, hard braking was correlated with both driver’s age and driver’s experience in the use of adaptive cruise control. This study analysed driver’s behaviour also concerning the gaze direction before approaching the intersection. Results highlighted that, during intentional car following mode, drivers noticed the presence of the turning car later in respect to the other driving conditions. The results found were used to develop a model able to reproduce and predict the driver’s reactions referred to the critical scenario analysed. The model was designed as a feedforward back-propagating artificial neural network. It is capable of computing outputs represented by brake reaction time, maximum deceleration and mean brake pedal force, depending on driving condition, time to intersection, perception time and driver’s profile. The obtained results, both in the analysis and in the modelling, aim to contribute to the development of advanced driving assistance systems. In particular, the present study could support the design of emergency vehicle warning systems in order to improve driving safety during critical scenarios.
Transport , Hållbar utveckling , Farkostteknik , Transport , Sustainable Development , Vehicle Engineering