text: With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. A common answer is that they should be adopting their users' driving style. This makes the assumption that users want their autonomous cars to drive like they drive - aggressive drivers want aggressive cars, defensive drivers want defensive cars. In this paper, we put that assumption to the test. We find that users tend to prefer a significantly more defensive driving style than their own. Interestingly, they prefer the style they think is their own, even though their actual driving style tends to be more aggressive. We also find that preferences do depend on the specific driving scenario, opening the door for new ways of learning driving style preference.
  @cite_4: Driver modeling is important for both automobile industry and intelligent transportation. One of its key topics has been studied in this paper, i.e., the style-oriented driver modeling for speed control, and a modeling scheme based on distal learning control and real-world vehicle test data (VTD) is proposed to make this possible. The driver model adapts to be an inverse model of the vehicle at run, which is accomplished under the distal guidance of the discrepancy between the actual and the desired vehicle speed. To tackle the divergence and local mutability of real-world vehicle data, the partly connected multilayered perceptron (PCMLP), which is a locally designed neural network, is utilized to fulfill the imitating of human operations on gas or brake pedals during real-world driving. The FTP-75 driving cycles are borrowed to test the established driver models, and driving styles of the original human drivers are retained and reproduced while accomplishing the speed following task. Simulations are conducted to verify the effectiveness of the proposed scheme.
  @cite_7: Driving aggressively increases the risk of accidents. Assessing a person's driving style is a useful way to guide aggressive drivers toward having safer driving behaviors. A number of studies have investigated driving style, but they often rely on the use of self-reports or simulators, which are not suitable for the real-time, continuous, automated assessment and feedback on the road. In order to understand and model aggressive driving style, we construct an in-vehicle sensing platform that uses a smartphone instead of using heavyweight, expensive systems. Utilizing additional cheap sensors, our sensing platform can collect useful information about vehicle movement, maneuvering and steering wheel movement. We use this data and apply machine learning to build a driver model that evaluates drivers' driving styles based on a number of driving-related features. From a naturalistic data collection from 22 drivers for 3 weeks, we analyzed the characteristics of drivers who have an aggressive driving style. Our model classified those drivers with an accuracy of 90.5 (violation-class) and 81 (questionnaire-class). We describe how, in future work, our model can be used to provide real-time feedback to drivers using only their current smartphone.
  @cite_17: This research effort provided valuable insight into the nature and severity of lane changes in a naturalistic driving environment. Sixteen commuters who normally drove more than 25 miles (40 km) in each direction participated. The two research vehicles were a sedan and a sport utility vehicle; each participant drove each vehicle for ten days. Data gathering was automatic, and no experimenter was present in the vehicle. There were 8,667 lane changes observed over 23,949 miles of driving, making this the largest known data collection effort for the study of lane changes. Analysis of the full data set resulted in many interesting findings regarding the frequency, duration, urgency, and severity of lane changes in regard to maneuver type, direction, and other classification variables. A subset of the full data set (500 lane changes) was then analyzed in greater depth using the sensor data collected by the instrumented vehicle. The sampled lane changes were generally of the more severe and urgent types since these are the cases in which a lane change collision avoidance system (CAS) is likely to be of greatest help. Variables analyzed for the sampled lane changes included turn signal use, braking behavior, steering behavior, eye glance patterns, and forward and rearward area analysis. The concept of a safety envelope for lane changes was then developed using the forward and rearward area analyses. Finally, the data were used to provide recommendations for designers of a lane change CAS in terms of display location and activation criteria. Overall, the research described in this report provides insight into the behaviors and parameters associated with lane changes, while the naturalistic data archive has the potential to address other questions related to driving behavior.
  @cite_23: 
  @cite_2: Abstract Two studies were conducted in order to develop a multidimensional instrument of driving style. In Study 1, we developed a self-report scale assessing four broad domains of driving style—the multidimensional driving style inventory (MDSI). A factor analysis revealed eight main factors, each one representing a specific driving style—dissociative, anxious, risky, angry, high-velocity, distress reduction, patient, and careful. In addition, significant associations were found between the eight factors, on the one hand, and gender, age, driving history, and personality measures of self-esteem, need for control, impulsive sensation seeking, and extraversion, on the other. In Study 2, further associations were found between the eight driving style factors and measures of trait anxiety and neuroticism. The discussion focused on the validity and utility of a multidimensional conceptualization of driving style.
  @cite_16: The aim of this study was to validate the stability of the different factors of the Multidimensional Driving Style Inventory (MDSI) [16], which was originally developed and validated with participants in different geographical areas of Israel. In this study, the questionnaire was distributed in the Netherlands and Belgium. A factor analysis of the data of 364 participants revealed five of the eight factors that resulted from the original factor analysis: Angry driving, Anxious driving, Dissociative driving, Distress-reduction driving, and Careful driving style. In addition, 24 items divided over the five factors seem to be stable compared to the 44 items divided over the eight factors of the original analysis. The factors revealed through the analysis of these data were used to determine driver profiles, consisting of one or two driving styles. The next step is to compare self-report data on driving style to actual driving behaviour.
  @cite_20: 
  @cite_11: The experiment investigates the effect of perceived control on risk taking in a dynamic, everyday task. Using established and validated video simulation techniques, the risk-taking preferences for 96 drivers were measured for a range of driving activities (speed choice, following distance, gap acceptance, and overtaking). The perceived control manipulation was as follows: Half of the participants were told to imagine they were driving the vehicle, and the other half were told to imagine they were passengers. Those who were told to imagine they were driving chose significantly faster speeds than did those who were told to imagine they were passengers. Differences for the other risk-taking measures were not significant. For speed choice, it could be argued that an illusion of control was in operation, such that those who were in control (i.e., drivers) were comfortable with a higher level of risk than those who were not in control (i.e., passengers).
summary: Defensiveness-aggressiveness is the most commonly used metric for defining driving style. Prior work refers to drivers as aggressive assertive versus defensive @cite_20 ; or mild versus moderate versus aggressive @cite_4 . In the Multidimensional Driving Style Inventory (MDSI), Taubman-Ben-Ari identified four broad driving styles: (1) reckless and careless driving, characterized by, for example, higher speed; (2) anxious driving; (3) angry and hostile driving, characterized by more use of the horn and flash functionality; and (4) patient and careful driving @cite_2 . Similarly, Huysduynen categorized driving style as angry driving, anxious driving, dissociative driving, distress-reduction driving and careful driving style @cite_16 . Horswill provided a valuable distinction between skill and style in the context of driving behaviors @cite_11 . Hong @cite_7 differentiated styles in terms of defensiveness, as well as by propensity for violation of rules. Scherer defined driving style in terms of comfort @cite_23 . Lee @cite_17 analyzed lane changes as a function of its severity (degree to which the vehicle in the destination lane was cut off), urgency (how soon the lane change was needed), and type classification for the full population of 8,667 lane changes.