Technological advancements focus on developing comfortable and acceptable driving characteristics in autonomous vehicles. Present driving functions predominantly possess predefined parameters, and there is no universally accepted driving style for autonomous vehicles. While driving may be technically safe and the likelihood of road accidents is reduced, passengers may still feel insecure due to a mismatch in driving styles between the human and the autonomous system. Incorporating driving style preferences into automated vehicles enhances acceptance, reduces uncertainty, and poses the opportunity to expedite their adoption. Despite the increased research focus on driving styles, there remains a need for comprehensive studies investigating how variations in the driving context impact the assessment of automated driving functions. Therefore, this work evaluates lateral driving style preferences for autonomous vehicles on rural roads, considering different weather and traffic situations. A controlled study was conducted with a variety of German participants utilizing a high-fidelity driving simulator. The subjects experienced four different driving styles, including mimicking of their own driving behavior under two weather conditions. A notable preference for a more passive driving style became evident based on statistical analyses of participants' responses during and after the drives. This study could not confirm the hypothesis that subjects prefer to be driven by mimicking their own driving behavior. Furthermore, the study illustrated that weather conditions and oncoming traffic substantially influence the perceived comfort during autonomous rides. The gathered dataset is openly accessible at https://www.kaggle.com/datasets/jhaselberger/idcld-subject-study-on-driving-style-preferences.

Advancements in technology are steering attention toward creating comfortable and acceptable driving characteristics in autonomous vehicles. Ensuring a safe and comfortable ride experience is vital for the widespread adoption of autonomous vehicles, as mismatches in driving styles between humans and autonomous systems can impact passenger confidence. Current driving functions have fixed parameters, and there is no universally agreed-upon driving style for autonomous vehicles. Integrating driving style preferences into automated vehicles may enhance acceptance and reduce uncertainty, expediting their adoption. A controlled vehicle study (N = 62) was conducted with a variety of German participants to identify the individual lateral driving behavior of human drivers, specifically emphasizing rural roads. We introduce novel indicators for assessing stationary and transient curve negotiation, directly applicable in developing personalized lateral driving functions. To assess the predictability of these indicators using self-reports, we introduce the MDSI-DE, the German version of the Multidimensional Driving Style Inventory. The correlation analysis between MDSI factor scores and proposed indicators showed modest but significant associations, primarily with acceleration and jerk statistics while the in-depth lateral driving behavior turned out to be highly driver-heterogeneous. The dataset including the anonymized socio-demographics and questionnaire responses, the raw vehicle measurements including labels, and the derived driving behavior indicators are publicly available at https://www.kaggle.com/datasets/jhaselberger/spodb-subject-study-of-lateral-vehicle-guidance.

Texas A&M is playing a leading role in expanding the capabilities of automated vehicles and investigating how they can be safely used on rural roads. The Texas A&M Engineering Experiment Station (TEES) was recently awarded $7 million in federal grant funding from the Department of Transportation (DOT). In partnership with researchers from George Washington University and the University of California-Davis, A&M professors will be studying the specifics of how automated vehicles work on rural roadways -- something Alireza Talebpour, assistant professor in the Department of Civil and Environmental Engineering, said not much is currently known about. "Autonomous vehicle testing has pretty much only been done in urban centers," Talebpour said. "The technology is useless if it only works in big cities – the majority of roads in the United States are rural. We want to enable autonomous driving for people who don't live in big cities."

Victoria has sanctioned a trial of driverless cars on rural roads in a bid to improve the dramatically more dangerous conditions outside urban areas. People are five times more likely to be killed on a Victorian country road than in the city. The automated vehicle technology is being developed by Bosch as part of a $2.3m state government grant and will be tested on high-speed rural roads later this year. "This trial is an exciting step towards driverless vehicles hitting the road," the acting premier, Jacinta Allan, said. Bosch has been granted the state's first permit to allow automated vehicles for on-road testing, with other successful applicants to be announced soon.

If you hope to ride in a driverless car someday, chances are that the trip will take place in an urban area. When offering jaunts to the public in autonomous cars, companies like Waymo and Drive.ai And Cruise, a part of General Motors, plans to offer an autonomous taxi service next year in a major U.S. city. But what about running an autonomous car on a stretch of rural road--just an asphalt strip with natural objects like grass and trees nearby, and no detailed, three-dimensional map for the vehicle to reference? Researchers from MIT have been working on that problem, and their strategy involves teaching cars to drive like humans.