471 results about "Driving risk" patented technology

Managing a vendor may comprise identifying a product to be supplied to an entity. Risk elements associated with the product may be determined by performing a product category-driven risk assessment. A transaction level risk assessment is performed for one or more vendors proposed to supply the product to the entity. At least one management action is determined for reducing risk for a risk element of the plurality of risk elements associated with supplying the product to the entity by a selected vendor from a group comprising the one or more vendors proposed to the supply the product to the entity, wherein the at least one management action is determined according to results of the product category-driven risk assessment and the transaction level risk assessment for the selected entity. A vendor level quality plan for the selected vendor is created that comprises the at least one management action.

Systems and methods for taking risk out of driving are presented. The system comprises an event capture device that records audio, video, and other information that collectively comprise an event. The event data, including the audio, video, and other related information, is provided to an evaluation server where it is stored in a database of events. Driving events can be analyzed singularly or collectively to provide counseling of fleet drivers, reconstruction and forensic analysis of automobile accidents, and driver and/or vehicle scores/ratings.

The present invention relates generally to the field of improvement in the driving risk assessment arts. More particularly, but not by way of limitation, the present invention generally relates to a method of rating the driving risks of individuals based on that driver's actual driving record as obtained from his or her fellow drivers.

Systems and methods for scoring individual and groups of drivers and providing vehicle ratings based on captured driving event data are provided. The system comprises an event capture device that records both audio and video of an event. The event data, which includes the audio, video, and other related information, is provided to an evaluation server where it is compiled and analyzed to identify correlations between events, driving behavior, vehicles, circumstances of events, individual drivers, and other event attributes. These correlations are continuously updated and can be weighted in order to provide a comprehensive analysis of the tendencies and characteristics of an individual driver or group of drivers. Event data particular to an individual or group is compiled and analyzed to provide an individualized driver or group score that can be used as a basis to rate the driver or group for customized insurance coverage.

Systems and methods for the capture, analysis, and reporting of risky driver behavior are provided in combination with coaching of individual drivers to improve driver performance. The system comprises an event capture device that records both audio and video of an event. The event data, which includes the audio, video, and other related information, is provided to an evaluation server where it is then analyzed and scored and compiled with other event data. Event data can be compiled for individual drivers, groups of drivers, and the entire universe of drivers for an organization. The evaluation server provides managers, supervisors, and executives with access to reports including granular information about risky behavior and trends related to individual drivers, groups of drivers, or the entire universe of drivers for the organization. Additionally, the evaluation server provides coaching sessions for drivers (with or without their supervisors) to demonstrate to the driver with audio, video, and other related information (e.g., speed) how the event occurred and improved ways to avoid such an event in the future. In this fashion, driver improvement is achieved and insurance costs may be reduced for the organization.

Described herein are a system and method of training a motor vehicle operator in crash-avoidance skills. Critical crash-avoidance skills comprise actively scanning a driving environment to identify, recognize, and remember what is seen; adjusting vehicle speed and position to accommodate road conditions, visibility, and traffic; proactively identifying and responding to potential hazards before the potential hazards become immediate hazards; recognizing, assessing, and responding to driving risks; creating a space cushion in order to identify and maintain possible escape routes; and identifying and minimizing visual, manual, and cognitive distractions. Training comprises at least one interactive driving simulation in which use of technology by a motor vehicle operator impacts performance of the motor vehicle operator. The training comprises computer-based learning tutorials, interactive point-of-view driving simulations, and conjoint experienced driver mentor/inexperienced trainee activities, as well as objective testing of motor vehicle operators on skills and driving knowledge.

A system and method for computer assisted event based reconstruction and forensic analysis of vehicle accidents is provided. The system comprises an event capture device that records audio, video, and other information that collectively comprise one or more events related to a vehicle accident. The event data, including the audio, video, and other related information, is provided to an evaluation server where it is stored in a database for forensic analysis of the events comprising the vehicle accident. Event data for a specific automobile accident event is analyzed and compared to similar types of automobile accident event data in order to forensically analyze the specific event and correlate causal relationships with key elements of the event data to determine the likely cause of the accident or the factors contributing to the accident. Correlation information is also stored at the evaluation server to provide historical data points about causal relationships and key elements.

The invention discloses a prediction method of driving risk based on a hidden Markov model. The method comprises the steps of (1) classifying driving risk states through a cluster analysis method based on vehicle operating characteristics, (2) estimating the influences of a driver behavior and surrounding traffic environment characteristics on a transition probability between driving risk states through multiple logistical models for different driving risk states, (3) with a risk state as a hidden state, with an actual observed vehicle movement variable as a state output value, with multiple logistic model parameters as parameter initial values of a state transition probability matrix, establishing a hidden Markov chain model that reflects the evolution rule of driving states, and (4) obtaining the vehicle operating characteristics in real time and predicting a future risk state based on the hidden Markov chain model. According to the method, the hidden Markov model which can reflect the above characteristic real-time change and has a variable state transition probability is established, the accuracy and prediction accuracy of a driving risk model are improved, and the real-time requirements of anti-collision warning can be satisfied.

The invention provides a driving behavior safety evaluation method based on human-vehicle-environment multiple data sources and belongs to the field of automobile safety driving research in the aspectof road transportation application. The method comprises the steps that information data of a driver, driving data of a vehicle and environmental factor data are acquired as automobile driving sourcedata; according to the automobile driving source data, related factors which influence the driving safety are analyzed, and a driving risk evaluation index system is built; through statistical analysis, an evaluation value corresponding to an evaluation index is extracted in the source data, a driving risk score of a vehicle to be evaluated is calculated and obtained, and finally the driving behavior safety grade of the vehicle is determined. The driving behavior safety evaluation method has reliable evaluation results, can prevent dangerous driving behaviors, reduces traffic accidents and improves the traffic flow.

The invention discloses a system and method for calculating driving risks and assisting automobile insurance pricing based on multi-source data. The system includes an intelligent mobile internet terminal and a remote server connected with each other; a plurality of data sensing acquisition units and applications are installed in the intelligent mobile terminal; and the remote server is provided with a risk model algorithm system and includes a scene driving risk analysis sub module, a distracted driving model recognition sub module, a user travel behavior analysis sub module and a driving behavior evaluation and grading system sub module. According to the system and method of the invention, data acquired by the intelligent mobile internet terminal and road traffic information acquired by the remote server are analyzed, and the scores of the sub modules are calculated, and automobile insurance pricing is carried out according to the scores. With the system and method of the invention adopted, based on multi-source data acquisition and fusion, the driving behaviors of users are analyzed, and the travel habits, driving habits and driving risks of the drivers can be effectively calibrated, and therefore, theoretical basis and technical support can be provided for driving behavior-based automobile insurance pricing models.

The invention relates to a device for preventing faulty operation for using an accelerator as a brake by mistake based on computer vision, which comprises a vision sensor, an embedded system, a vehicle speed measuring unit, a vehicle driving angle measuring unit, a distance measuring unit, a voice alarm unit, an accelerator control unit, a brake control unit and a power supply unit, wherein the embedded system is used for reading the information of driving speed of a vehicle, a vehicle steering wheel angle, a video image of a front road of the vehicle, and the like, estimating a barrier distance on the front road and calculating a driving risk factor and driving risk change tendency according to the barrier distance, the vehicle steering wheel angle and the speed of the vehicle. The invention makes a corresponding driving decision according to the driving risk factor and the driving risk change tendency, controls the voice alarm unit to produce drive warning sound according to the driving decision, controls the accelerator control unit to allow the action effectiveness of stepping the accelerator by a driver and controls the brake control unit to control the automatic brake of the vehicle.

The invention relates to a monitoring and evaluation method of driving behavior risk degrees and belongs to the technical field of automobile active safety. The method includes that steering wheel rotating angles, vehicle speed, longitudinal acceleration and transverse acceleration are real-timely collected; the real-timely collected data are arranged to form a data sequence in chronological sequence; driving risk degrees of acceleration, braking, overspeed, lave maintaining and lane changing are monitored and evaluated respectively according to vehicle state parameter and driver operation parameter information; comprehensive monitoring and evaluation of total driving risk degree at current moment is performed according to each risk degree index that is obtained in time window wide range so that a comprehensive monitoring and evaluation value at the current moment is obtained. Communication of the obtained comprehensive monitoring and evaluation value with a driver is achieved through an on-line real-time early warning man-machine interaction mode, and thereby, purposes of improving driving safety qualities and reducing road traffic accidents can be achieved.

The invention relates to the technical field of driving safety, in particular to a big data-based driving risk assessment method. The big data-based driving risk assessment method comprises the steps of performing long-term accumulation on driving risk assessment data, including risk assessment factors such as vehicle condition data of vehicles, driving behavior data of vehicle drivers, weather condition data of places in which the vehicles are located, road condition data of roads on which the vehicles are located, accident data of the vehicles and the like, of a sufficient number of vehicles; performing big data risk analysis on the risk assessment factors to obtain a relationship between each risk assessment factor and driving risk; generating a driving risk assessment model according to the relationship between each risk assessment factor and the driving risk; and finally performing risk assessment on real-time driving risk assessment data of a target vehicle by utilizing the driving risk assessment model to obtain real-time driving risk of the target vehicle. According to the technical scheme, the factors such as driving behaviors of the drivers, weather and the like are considered, so that the driving risk can be more accurately predicted in comparison with the prior art.

The invention discloses a method and a device for operating an autonomous vehicle. According to one embodiment, the method comprises the following steps: collecting driving schemes adopted by a driver when the driver performs exception interfering on semi-autonomous vehicle and driving scene information of the semi-autonomous vehicle in driving scenes; determining the risk grades of driving risks of the semi-autonomous vehicle in the driving scenes according to the driving schemes adopted by the driver; decomposing object information of all scene objects in the driving scenes from the driving scene information; learning the object information and the corresponding risk grades, recognizing risk objects relevant to the risk grades in the scene objects, and determining the association relationship between the object information of the risk objects and the risk grades, so as to serve as a risk assessment strategy of the autonomous vehicle; recognizing the risk grade of the driving risk of the autonomous vehicle through the risk assessment strategy. The embodiment realizes the automatic recognition of the driving risk of the autonomous vehicle.

The invention discloses a driving risk evaluation method and a driving risk evaluation system and relates to the field of traffic risk evaluation. The driving risk evaluation method comprises the following steps: acquiring driving information of a vehicle; processing driving information so as to obtain abnormal driving information of the vehicle; acquiring accident information, and establishing amachine learning model for evaluating driving risks according to the abnormal driving information and the accident information; evaluating the driving risks of the vehicle according to the machine learning model. By adopting the driving risk evaluation method and the driving risk evaluation system disclosed by the invention, driving risk evaluation results with very high precision can be acquired,risks of an evaluated vehicle can be accurately and objectively evaluated, fine requirements on risk evaluation are met, adverse driving behaviors of drivers can be corrected in time, and accident risks can be reduced.

The invention discloses an optimization method of vehicle collision early-warning and vehicle speed guidance. The method comprises: obtaining all target vehicle signals in a set range by a main vehicle, and forming a target signal list according to obtaining time; obtaining running status of the main vehicle in real time, and using a manner of GPS and DR combination navigation to optimize a running track and the running status of the main vehicle; using running status quantity of the main vehicle after filtering to estimate and optimize current road curvature; using the main vehicle and targetvehicles to calculate relative position relationships, and combining the road curvature to carry out judgment of relative lanes, running directions and orientations; classifying the target signals, and if a target is a vehicle, carrying out track-based prediction, and carrying out collision early-warning through calculating collision time and collision distance; if the target is a roadside unit,carrying out corresponding roadside-unit reminding and vehicle speed guidance; and obtaining an alarming information list of the target signals. Adoption of the technical solution for automatic driving has a very good auxiliary effect, provides more accurate early-warning information, and reduces driving risks.

The invention discloses a vehicle driving risk evaluation method considering a driving style, which overcomes the problems that the prior art does not take into account the differences of driving habits of drivers with different styles, and the evaluation method is not personalized and humanized. The method comprises the following steps: 1) evaluating the driver's radical degree by using historical driving control data; 2) predicting that driving state base on the real-time vehicle running data and using the hidden Markov model; 3) for different driving states, establishing the fuzzy controller with different characteristic parameters to evaluate the driving risk; 4) using the driver's radical degree to correct the drive risk degree to obtain the final driving risk degree: carrying out thesuperposition correction of the driver's radical degree on the value of the drive risk degree in the range of [0.3, 0.7], and obtaining the final driving risk degree output result; therefore, for drivers with high or lower aggressiveness, the driving risk value within [0.3, 0.7] will increase or decrease.

The invention relates to a driving risk analysis and risk data sending method and device and belongs to the Internet of vehicles technology field. The method comprises steps that the position of firstequipment is acquired, and the first equipment is equipment of which risk analysis is to be carried out; first risk areas are determined according to vehicle driving lines corresponding to the position, and the first risk areas are areas having influence on driving behaviors of location vehicles of the first equipment; the first risk areas are screened to acquire risk data; the risk data is sentto the first equipment; the risk data comprises the vehicle, pedestrian and obstacle state data having bump risks with the location vehicles of the first equipment and traffic environment data havinginfluence on the driving behaviors of the location vehicles of the first equipment. The method is advantaged in that data sending time delay is reduced, bandwidth requirements of mutually-notified vehicle state data among the equipment are reduced, air interface resource scheduling frequency requirements are further reduced, and communication performance is improved.

The embodiment of the present invention discloses a driving risk assessment method and device. The method comprises the steps of: obtaining first driving data of a vehicle to be assessed, second driving data of a moving target, traffic environmental data of a target region and historical road accident data of the target region; determining a dynamic risk factor according to the first driving dataof the vehicle to be assessed and the second driving data of the moving target; determining a semi-dynamic risk factor according to the traffic environmental data; determining a static state risk factor according to the historical traffic accident data and the traffic environmental data; fusing the dynamic risk factor, the semi-dynamic risk factor and the static state risk factor to obtain a comprehensive risk factor; and determining a traffic risk grade according to the comprehensive risk factor. The driving risk assessment method and device combines various factors influencing the crossing risks based on the complexity and the variability of the crossing condition to determine the final traffic risk grade, and therefore, the driving risk assessment method and device can accurately and effectively prompt the risks to drives to avoid the traffic accidents.