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Accommodating Drivers’ Preferences Using a Customised Takeover Interface on UK Motorways
Published in Neville A. Stanton, Kirsten M. A. Revell, Patrick Langdon, Designing Interaction and Interfaces for Automated Vehicles, 2021
Nermin Caber, Patrick Langdon, Michael Bradley, James W.H. Brown, Simon Thompson, Joy Richardson, Jisun Kim, Lee Skrypchuk, Kirsten M. A. Revell, P. John Clarkson, Neville A. Stanton
Once in the car, participants took place in the passenger seat and were introduced to the specifications of the car and its interface by the safety driver. Following the introduction, the safety driver drove onto the test track, explained the essential car controls, and demonstrated semi-autonomous driving, control transitions from and to the car, and the cognitive load task. After the safety driver’s demonstration, the participant took place in the driver seat and experienced the previously mentioned situations on the test track. When the participant felt safe to operate the car on an open road, the participant left the test track facility and drove towards a service station from where the study started.
Innovative Environmental Design in Means and Systems of Transport with Particular Emphasis on the Human Factor
Published in Gavriel Salvendy, Advances in Human Aspects of Road and Rail Transportation, 2012
Grabarek Iwona, Choromanski Wlodzimierz
Functionalities shown in figure 2 can be achieved by a system of movable and foldable seats. This design, along with the microprocessor-control system, is considered a significant innovation by the authors. The steer-by-wire technology applied in the design of the Eco-car controls wheels’ turning angles, enhances driving comfort and allows the vehicle control system to intervene independently from the driver. The brake-by-wire technology controls the braking system and enables interventions independent from the driver, in order to adjust the braking force of each wheel to road conditions. The pneumatic suspension allows the lowering of the vehicle platform, shock-absorption and maintaining a constant ground-clearance.
GIDS architecture
Published in John A. Michon, Generic Intelligent Driver Support, 1993
Ep H. Piersma, Sjouke Burry, Willem B. Verwey, Wim van Winsum
The active car controls are the accelerator pedal and steering wheel (Chapter 7.3). Strictly speaking these are part of the user interface. The steering wheel is used for suggesting steering actions to the driver by discrete pulses. The accelerator pedal is used in two ways. One is by discrete changes in counterforce, the other by coupling the counterforce continuously to a variable of the driving task (Farber et al., 1990), in the GIDS prototype, the degree to which the maximum velocity allowed is exceeded. See Sections 8.8 and 8.9 for details concerning hardware.
From Video to Hybrid Simulator: Exploring Affective Responses toward Non-Verbal Pedestrian Crossing Actions Using Camera and Physiological Sensors
Published in International Journal of Human–Computer Interaction, 2023
Shruti Rao, Surjya Ghosh, Gerard Pons Rodriguez, Thomas Röggla, Pablo Cesar, Abdallah El Ali
The car driving simulator developed using AirSim13 recorded participants’ speed (m/s) and braking behavior (0 indicating no brake to 1 indicating full brake). Kinematic quantities of position, orientation and linear velocity were recorded using the North East Down (NED) coordinate system. The car controls were set to automatic driving, and the positions and orientations were aligned as per the location of the road segment provided within the city environment. We analyzed the driving behavior of participants with respect to the three pedestrian action types (pos, non_pos, no_action). Particularly, we investigated the change in mean velocity (m/s) and braking behavior of the participants after encountering different actions (Zhao et al., 2021). This is because driving velocity and braking have been shown to serve as reliable indicators for identifying a range of driver emotions (Roidl et al., 2014; Schmidt-Daffy, 2012). The mean velocity (m/s) for the three action types are—pos: 12.80 m/s, non-pos: 13.10 m/s, no-action: 12.50 m/s and the standard deviations of velocity are—pos: 1.8 m/s, non-pos: 1.89 m/s, no-action: 1.78 m/s. The mean braking (0–1) for the three action types are—pos: 0.71, non-pos: 0.68, no-action: 0.74 and the standard deviations of braking (0–1) are—pos: 0.20, non-pos: 0.21, no-action: 0.17. Figure 17 summarizes the mean velocity and brake across the three action types.
Experience-Based Cognition for Driving Behavioral Fingerprint Extraction
Published in Cybernetics and Systems, 2020
Haoxi Zhang, Fei Li, Juan Wang, Yang Zhou, Cesar Sanin, Edward Szczerbicki
Recently, together with the rapid progress of information technologies and the advances in vehicle active control and advanced driver assistance systems, there have been significant research efforts in assisting the human driver in vehicle control actions. These advances allow artificial intelligence software to be embedded in cars and to learn the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through their operational data of driving process. More importantly, knowledge of the driver’s driving behavioral fingerprint can facilitate the interaction between automobiles and human drivers so that better and safer car controls can be made, for example: predicting a driver’s intended actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents (Liu 2001; Olabiyi et al. 2017).
Smart Data, Information, and Knowledge Processing for Intelligence Amplification: Approaches, Models and Case Studies
Published in Cybernetics and Systems, 2020
Edward Szczerbicki, Ngoc Thanh Nguyen, Cecilia Zanni-Merk
The authors of the following paper titled “Experience-based Cognition for Driving Behavioral Fingerprint Extraction” introduce a novel approach to extract the driver’s driving manners capture and representation. The approach is based on the framework called Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience. Such experience is later used to develop better and safer car controls and to enhance vehicle smartness.