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.
The typical behavioral patterns of a driver are usually referred to by the term . This includes the choice of driving speed, headway, overtaking of other vehicles, or the tendency to commit traffic violations @cite_16 .
@cite_16
2018690964 2761751533 2104425135 1983738939
Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone. People's driving behavior is influenced by different human and environmental factors, and several attempts to characterize it have been proposed. Nowadays, the standardization of the CAN bus and the increase of the electronic components units in modern cars offer a large availability of sensors data that make possible a more reliable and direct characterization of driving styles. In this work, we propose the concept of "Driving DNA" as a way of describing the complexity of driving behavior through a set of individual and easy-to-measure quantities. These quantities are responsible for some aspects of the driver's behavior, just as -- in the metaphor -- genes are responsible for the tracts of an individual. The concept has been tested on a dataset collected from the CAN bus consisting of more than 2000 trips performed by 53 people, in a wide scenario of road types and open traffic conditions. The Driving DNAs have been calculated for each person, and a graphical visualization of their comparison is provided. A method for detecting drivers’ intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs. 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.