Abstract
There is not yet sufficient knowledge on how people want to be driven in a highly automated vehicle. Many studies suggest that automated vehicles should drive like a human driver, e.g. moving to the right edge of the lane when meeting oncoming traffic. To generate naturally looking trajectory behaviour, more detailed studies on manual driving are necessary. The authors report on a driving simulator study investigating twelve different oncoming traffic scenarios. 46 subjects experienced scenarios with variations in type of vehicle (trucks, cars), quantity (one, two) and position (with/without lateral offset) – each on a lane 3.00 m or on 2.75 m wide respectively. Results show that subjects react to oncoming traffic by veering to the right edge of the lane. We also found that quantity, type, and position of oncoming vehicles influence manual driving behaviour. Trucks and vehicles with lateral offset to the road centre lead to greater reactions and hence to more lateral distance between the ego and the oncoming vehicle. From this study on manual driving, we recommend an adaptive autonomous driving style which adjusts its trajectory behaviour on type and position of oncoming vehicles. Thus, our results help to design an accepted and trusted trajectory behaviour for highly automated vehicles.
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Acknowledgements
This research was partially supported by the German Federal Ministry for Economic Affairs and Climate Action (research project: STADT:up, funding code: 19A22006R). The sponsor had no role in the study design, the collection, analysis and interpretation of data, the writing of the report, or the submission of the paper for publication. We are very grateful to Maximilian Hentschel for his assistance in driving simulation programming and to Samuel Pollmer for data collection and analysis.
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Rossner, P., Friedrich, M., Felbel, K., Dettmann, A., Bullinger, A.C. (2023). I Also Care in Manual Driving - Influence of Type, Position and Quantity of Oncoming Vehicles on Manual Driving Behaviour on Straights on Rural Roads. In: Duffy, V.G., Krömker, H., A. Streitz, N., Konomi, S. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14057. Springer, Cham. https://doi.org/10.1007/978-3-031-48047-8_25
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