A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Insurance Applications <s> This paper describes the potential of in-vehicle data recorder (IVDR) systems to be used in various commercial and research applications as tools to monitor and provide feedback to drivers on their on-road behavior. The implementation of IVDR is demonstrated using the example of the DriveDiagnostics system. This system can identify various maneuver types that occur in the raw measurements, and use this information to calculate risk indices that indicate on the overall trip safety. Drivers receive feedback through various summary reports, real-time text messages or an in-vehicle display unit. Validation tests with the system demonstrate promising potential as a measurement tool to evaluate driving behavior. Reductions in crash rates and the risk indices are observed in the short-term. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Insurance Applications <s> We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Insurance Applications <s> Profiling driving behavior has become a relevant aspect in fleet management, automotive insurance and eco-driving. Detecting inefficient or aggressive drivers can help reducing fleet degradation, insurance policy cost and fuel consumption. In this paper, we present a Fuzzy-Logic based driver scoring mechanism that uses smartphone sensing data, including accelerometers and GPS. In order to evaluate the proposed mechanism, we have collected traces from a testbed consisting in 20 vehicles equipped with an Android sensing application we have developed to this end. The results show that the proposed sensing variables using smartphones can be merged to provide each driver with a single score. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Insurance Applications <s> Assessment of driving behavior and estimation of risk therein is an important use case for telematics and vehicular networking technology domain, which has gained interest in the fleet management and consumer verticals, but above all with the car insurance firms. These firms have started incorporating the results of such analysis to provide their customers with "pay as you drive" insurance plans, which decide the initial premium based on the driver's past history of crashes and violations. Then the parties enter into an evaluation period of about two months (say), at the end of which premium is recalculated based on analysis of accelerometer, GPS and on-board diagnostic (OBD II) data. However, the consumer has no knowledge of the progress of his or her evaluation due to lack of access to device data from insurance provider. One major criterion of such evaluation is based on the detection and aggregation of high-risk maneuvers by the driver, which typically constitutes of hard bump, sharp cornering, hard stop and speed limit violations. In this paper, we propose a novel method named MobiDriveScore for routine ventures using which the consumer can assess his/her own driving pattern. Thus the consumer can consciously reduce the risk associated with his/her driving by using MobiDriveScore. For MobiDriveScore, detection of maneuvers is done using accelerometer and GPS sensors only, which are integral part of modern day smartphones. Using MobiDriveScore, the user can choose an alternate route or choose to travel a little early to improve upon the premium rates and stay safe. It will also help aggressive drivers to become more passive. Experiments have been conducted and the initial results of MobiDriveScore are found to be encouraging. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Insurance Applications <s> This paper presents DriveSafe, a new driver safety app for iPhones that detects inattentive driving behaviors and gives corresponding feedback to drivers, scoring their driving and alerting them in case their behaviors are unsafe. It uses computer vision and pattern recognition techniques on the iPhone to assess whether the driver is drowsy or distracted using the rear-camera, the microphone, the inertial sensors and the GPS. We present the general architecture of DriveSafe and evaluate its performance using data from 12 drivers in two different studies. The first one evaluates the detection of some inattentive driving behaviors obtaining an overall precision of 82% at 92% of recall. The second one compares the scores between DriveSafe vs the commercial AXA Drive app obtaining a better valuation to its operation. DriveSafe is the first app for smartphones based on inbuilt sensors able to detect inattentive behaviors evaluating the quality of the driving at the same time. It represents a new disruptive technology because, on the one hand, it provides similar ADAS features that found in luxury cars, and on the other hand, it presents a viable alternative for the “blackboxes” installed in vehicles by the insurance companies. <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Insurance Applications <s> Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver. <s> BIB006
The insurance industry, especially the motor insurance sector, generally calculate their premiums based on statistical data through the evaluation of factors that are believed to impact expected cost of future claims. These factors include among others, the type of vehicle, the value and characteristics of the vehicle, as well as the profile of the driver (age, gender, marital status, driver experience, etc.), which do not always fairly represent each individual driver's propensity to risk. However, driving behaviour analysis can assist to provide a more accurate representation of an individual. The results in BIB006 indicated that dangerous driving events are accurately detected and provided a representative score for each individual driver. GPS vehicle tracking through vehicle-fixed "black box" units is promoted by insurers, as the information retrieved from these units can be used by the insurance companies to better estimate risk. The DriveDiagnostics system monitors on-road driver behaviour, calculates risk and provides relevant feedback using data from vehicle-fixed data recorders BIB001 . Methods based on vehicle-fixed sensor data acquisition are unappealing to some people who find the idea of constant monitoring invasive. Another approach recently exploit is behaviour-based insurance combined with usage based insurance (UBI), which makes use of driver behaviour patterns and driving style classification to improve assessment of driver risk and insured risk. An example of such an approach that moves away from vehicle-fixed systems is proposed in BIB002 (Figure 4) , where new degrees of freedom are introduced with a smartphone as sensing platform. The smartphone based system also functions as a driver assistance or support tool BIB005 . This type of system can afford the driver the chance to reduce his risk profile, and by doing so, qualify for a reduced insurance premium. Examples of such a system are the Pay-As-You-Drive (PAYD) and UBI schemes BIB003 . In PAYD insurance plans the initial premium is based on the driver's risk profile. The premium is then recalculated after a predefined evaluation period based on accelerometer, GPS and on-board diagnostics (OBD) data. The down side of this system is that the customer does not have an indication of his progress because the device data is not accessible by the insured party. MobiDriveScore was therefore developed in order to enable the customer to assess his personal driving pattern and score using a smartphone BIB004 . 