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
2045956037
In urban environments traffic safety is primarily determined by the successful interaction between road users. In order to define behavioral requirements for future autonomous vehicles, an understanding of these interactions is needed. Therefore this paper analyzes the interaction between drivers and pedestrians at crosswalks from both the driver's and the pedestrian's perspective. A study with driving experiments in real-life traffic was conducted. The study not only investigates how drivers respond to pedestrians who intend to use the crosswalk, but also their response to pedestrians who waive their right-of-way. An analysis of recorded driving and video data identified typical behavior patterns of drivers approaching a crosswalk, as well as of pedestrians crossing on it. Supplemented by questionnaires filled in by drivers and pedestrians, we determined factors which have a relevant impact on successful driver-pedestrian interaction. We confirmed that with higher initial speeds, drivers tend to exhibit more anticipatory driving behavior. Contrary to previous studies, the presented results show a significant influence of the vehicle's speed on the pedestrian's decision process. The gained insights into the driver-pedestrian interaction process provide an opportunity to develop autonomous driving functions with a cooperative driving behavior that conforms to the expectations of both pedestrians and vehicle occupants. 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). 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. Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles.