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.
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.
@cite_4 @cite_7 @cite_17 @cite_23 @cite_2 @cite_16 @cite_20 @cite_11
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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 stylethe multidimensional driving style inventory (MDSI). A factor analysis revealed eight main factors, each one representing a specific driving styledissociative, 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. 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. It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers. 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. Objective:The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety.Background:Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.Method:A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.Results:A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and ind... 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 dat 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. Several deep learning approaches have been applied to the autonomous driving task, many employing end-to-end deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This paper describes a technique for using Multi-Modal Multi-Task Learning that considers multiple behavioral modalities as distinct modes of operation for an end-to-end autonomous deep neural network utilizing the insertion of modal information as secondary input data. Using labeled data from hours of driving our fleet of 1 10th scale model cars, we trained multiple neural networks to imitate the steering angle and driving speed of human control of a car. We show that in each case, our models trained with MTL can match or outperform multiple networks trained on individual tasks, while using a fraction of the parameters and having more distinct modes of operation than a network trained without MTL on the same multi-modal data. These results should encourage Multi-Modal MTL-style training with the insertion of Modal Information for tasks with related behaviors.