Methods for adapting the reactions of an advanced driver assistance system

Abstract

A method for adapting the response of a vehicle equipped with an advanced driver assistance system (ADAS), which includes the collection of telemetry data. The telemetry data includes impact and near-impact events relative to a map. The method further includes the collection of environmental data and the analysis of the telemetry data in relation to the environmental data to determine impact risk factors. The impact risk factors correlate with an increased risk of impact. The method further includes the identification of areas with an increased risk of impact based on the impact risk factors. The method also includes determining the location of the ADAS-equipped vehicle relative to the map and the collection of vehicle data from one or more sensors.The procedure further includes calculating an increased risk of a vehicle impact index (IRVCI) based on the location of the ADAS-equipped vehicle and the collected vehicle data. The procedure further includes adjusting the response of the ADAS-equipped vehicle based on the IRVCI.

Description

Introduction

[0001] This description refers generally to an advanced driver assistance system (ADAS). More specifically, this description refers to a system and method that adapts the reactions of a vehicle equipped with ADAS.

[0002] Vehicles are equipped with ADAS to enhance vehicle and road safety. ADAS-equipped vehicles include sensors that collect data about the vehicle's surroundings. This data is processed to generate an alarm or an automatic vehicle response. While ADAS generates alarms and automatic vehicle responses, it does not adjust the timing of these alarms or responses to environmental factors or a driver's driving habits and preferences.

[0003] While current ADAS-equipped vehicles fulfill their intended purpose, there is therefore a need for a new and improved system and procedure to adapt the reactions of the ADAS-equipped vehicle based on environmental, system, and personalization factors. Description

[0004] A method for adapting the response of a vehicle equipped with an advanced driver assistance system (ADAS) is provided, based on several aspects. The method involves collecting telemetry data from a large number of remotely located vehicles. This telemetry data includes impact and near-impact events related to locations on a map. The method also includes collecting environmental data for these locations. This environmental data indicates inherent characteristics of the locations, including road curvature, gradient, surface, visibility, and weather conditions. Furthermore, the method involves performing an analysis of the telemetry data in relation to the environmental data to determine impact risk factors that correlate with an increased risk of impact.The procedure further includes identifying areas with an increased risk of impact based on impact risk factors. It also includes determining the location of the ADAS-equipped vehicle relative to the map. The procedure further includes collecting vehicle data from one or more sensors mounted on the ADAS-equipped vehicle. This vehicle data includes vehicle load, trailer status, tire pressure, tire wear, tire temperature, and whether a spare tire is fitted. The procedure further includes calculating an increased risk of vehicle crash index (IRVCI) based on the location of the ADAS-equipped vehicle relative to the areas with an increased risk of impact and the vehicle data. Finally, the procedure includes adjusting the response of the ADAS-equipped vehicle based on the IRVCI.

[0005] In an additional aspect of the present description, the procedure also includes the collection of telemetry data from a cloud.

[0006] In another aspect of the present description, the procedure also includes classifying the telemetry data as impact events when distant vehicles collide.

[0007] In another aspect of the present description, the procedure further includes classifying the telemetry data as near-collision events when the remote vehicles activate alarms indicating a near-collision.

[0008] In another aspect of the present description, the procedure further includes classifying the telemetry data as near-collision events when the remote vehicles activate automatic vehicle reactions that indicate a near-collision.

[0009] Another aspect of the present description includes impact risk factors such as road curvature, traffic patterns, and intersection configurations.

[0010] In another aspect of the present description, the procedure also includes locating the impact risk factors relative to the map.

[0011] In another aspect of the present description, the procedure also includes adjusting the reaction time control of the ADAS-equipped vehicle.

[0012] In another aspect of the present description, the procedure also includes adjusting the reaction aggressiveness of the ADAS-equipped vehicle.

[0013] A method for adapting the response of a vehicle equipped with an advanced driver assistance system (ADAS) is provided, based on several aspects. The method involves generating a driver profile. This profile is based on collected driving habits and preferences. The driver's driving habits include vehicle speed, acceleration, deceleration, and steering inputs over a period of time. The method further includes calculating a driver's historical aggressiveness metric (D-HAM) by comparing the driving habits to a statistical mean. Finally, the method involves collecting vehicle data from one or more sensors mounted on the ADAS-equipped vehicle.The vehicle data includes vehicle load, trailer status, tire pressure, tire wear, tire temperature, and fitted spare tires. The procedure further includes collecting environmental data from locations on a map. The environmental data indicates inherent properties of the locations. These inherent properties include road curvature, road gradient, road surface, visibility, and weather conditions. The procedure further includes collecting real-time vehicle inputs from the driver. These vehicle inputs include speed, acceleration, deceleration, and steering inputs relative to the map. The procedure further includes performing an analysis of the vehicle data and the environmental data to determine impact risk factors when the vehicle inputs deviate from the D-HAM. The procedure further includes calculating a predicted aggressiveness metric.The procedure involves adjusting the driver's predicted aggressiveness metric (D-PAM) based on the driver's impact risk factors and the deviation of the D-HAM. It also includes adapting the response of the ADAS-equipped vehicle based on the D-PAM.

[0014] Another aspect of this description involves driving preferences, which include settings that the driver has entered into the ADAS.

[0015] In another aspect of the present description, the method also includes the collection of biometric data from a variety of cabin sensors located inside the ADAS-equipped vehicle.

[0016] Another aspect of the present description involves calculating the D-HAM by comparing the driver's speed with the statistical mean.

[0017] Another aspect of the present description involves calculating the D-HAM by comparing the driver's acceleration with the statistical mean.

[0018] Another aspect of the present description involves calculating the D-HAM by comparing the driver's deceleration with the statistical mean.

[0019] Another aspect of the present description involves calculating the D-HAM by comparing the driver's steering inputs with the statistical mean.

[0020] Another aspect of the present description involves the detection of deviations from the D-HAM when the vehicle inputs are outside a certain range of the D-HAM.

[0021] In another aspect of the present description, the procedure also includes adjusting the reaction time control of the ADAS-equipped vehicle.

[0022] In another aspect of the present description, the procedure also includes adjusting the reaction aggressiveness of the ADAS-equipped vehicle.

[0023] A method for adapting the response of a vehicle equipped with an advanced driver assistance system (ADAS) is provided, based on several aspects. The method involves collecting telemetry data from a large number of remotely located vehicles. This telemetry data includes impact and near-impact events related to locations on a map. The method also includes collecting environmental data for these locations. This environmental data indicates inherent characteristics of the locations, including road curvature, gradient, surface, visibility, and weather conditions. Furthermore, the method involves performing an analysis of the telemetry data in relation to the environmental data to determine impact risk factors that correlate with an increased risk of impact.The process further includes identifying areas with an increased risk of impact based on risk factors. It also includes determining the location of the ADAS-equipped vehicle relative to the map. The process further includes collecting vehicle data from one or more sensors mounted on the ADAS-equipped vehicle. This vehicle data includes vehicle load, trailer status, tire pressure, tire wear, tire temperature, and whether a spare tire is mounted. The process further includes calculating an increased risk of vehicle crash index (IRVCI) based on the location of the ADAS-equipped vehicle relative to the areas with an increased risk of impact and the vehicle data. Finally, the process includes generating a driver profile. This driver profile is based on collected driving habits, biometric data, and driving preferences.The driver's driving habits include vehicle speed, acceleration, deceleration, and steering inputs over a period of time. The procedure further includes calculating a historical aggressiveness metric (D-HAM) for the driver by comparing the driving habits to a statistical mean. The procedure also includes collecting real-time vehicle inputs from the driver, including speed, acceleration, deceleration, and steering inputs relative to the map. The procedure further includes performing an analysis of the vehicle and environmental data to determine impact risk factors when the vehicle inputs deviate from the D-HAM. The procedure further includes calculating a predicted aggressiveness metric (D-PAM) for the driver from the impact risk factors based on the deviation from the D-HAM.The procedure also includes adjusting the response of the ADAS-equipped vehicle based on the IRVCI and the D-PAM.

[0024] Further areas of application will become apparent from the description provided herein. It is understood that the description and specific examples serve only as illustrations and are not intended to limit the scope of this description. Brief description of the drawings

[0025] The drawings described herein serve only for illustrative purposes and are not intended to limit the scope of the present disclosure in any way. Fig. Figure 1 is a schematic drawing of a system for adapting the reactions of a vehicle equipped with an Advanced Driver Assistance System (ADAS). Fig. Figure 2 is a flowchart of a procedure for adjusting the reactions of the ADAS-equipped vehicle. Fig. Figure 3 is a flowchart of another procedure for adjusting the reactions of the ADAS-equipped vehicle. Detailed description

[0026] The following description is merely exemplary and is not intended to limit the present description, application or uses.

[0027] With reference to Fig. Figure 1 shows a system 10 for adapting warnings and responses of an advanced driver assistance system (ADAS) 12 in a vehicle 14 equipped with an ADAS. The ADAS 12 can provide various levels of driving automation, including Level 5, Level 4, Level 3, and Level 2 automation. For example, a Level 5 system indicates "full automation," which refers to the full-time performance by an automated driving system of aspects of the dynamic driving task under a range of road and environmental conditions that can be managed by a driver 16. A Level 4 system indicates "high automation," which refers to the driving-mode-specific performance by an automated driving system of aspects of the dynamic driving task, even if the driver 16 does not respond appropriately to a request for intervention.In Level 3 vehicles, the vehicle systems perform the entire dynamic driving task (DDT) within the area for which they are designed. The driver is only expected to be responsible for DDT fallback if the ADAS-equipped vehicle essentially "requests" the driver to take over if something goes wrong or if the ADAS-equipped vehicle is about to leave the zone in which it can operate. In Level 2 vehicles, systems provide steering, braking / acceleration assistance, lane centering, and adaptive cruise control. However, even when these systems are activated, the driver must still be driving and continuously monitoring the automated features.

[0028] The ADAS 12 incorporates various actuator devices (not shown) used to achieve the automation levels described above. These actuator devices control one or more vehicle features, including, but not limited to, a drive system, a transmission system, a steering system, and a braking system (not shown). In various embodiments, the vehicle features may further include interior and / or exterior vehicle features, such as, but not limited to, doors, a trunk, and cabin features such as air conditioning, music, lighting, etc. Therefore, the alerts and responses of the ADAS 12 include, but are not limited to, a collision avoidance system (CAS), lane departure warning (LDW), adaptive cruise control (ACC), blind spot monitoring (BSM), and pedestrian detection.: pedestrian detection (PD), driver monitoring system (DMS), traffic sign recognition (TSR), automatic parking system (APS), lane keep assist system (LKA), automatic emergency braking (AEB), etc.

[0029] The ADAS-equipped vehicle 14 collects data through one or more sensors 18 located on and inside the ADAS-equipped vehicle 14. The one or more sensors 18 communicate with a controller 20. The multiple sensors 18 are configured to generate a signal indicating the observed conditions of the external and / or internal environment of the ADAS-equipped vehicle 14. The one or more sensors 18 may, but are not limited to, one or more radar units, one or more lidar sensors (light detection and ranging sensors), one or more proximity sensors, one or more odometers, one or more ground penetrating radar sensors (GPR sensors), one or more steering angle sensors, or one or more GPS transceivers.: global positioning system (GPS), one or more tire pressure sensors, one or more cameras (e.g., optical cameras and / or infrared cameras), one or more gyroscopes, one or more accelerometers, one or more inclinometers, one or more velocity sensors, one or more ultrasonic sensors, one or more inertial measurement units (IMUs), and / or other sensors. For clarity, in . Fig. 1 only one environmental sensor 22 and one vehicle sensor 24 of the one or more sensors 18 shown.

[0030] The environmental sensor 22 is located on the ADAS-equipped vehicle 14. The environmental sensor 22 detects observable conditions on the exterior of the ADAS-equipped vehicle 14, such as environmental data. This environmental data includes road and driving conditions surrounding the ADAS-equipped vehicle 14. Road conditions include, but are not limited to, road curvature, traffic patterns, intersection configurations, whether the road is paved or unpaved, and the presence of precipitation. Additionally, the environmental data includes information about road visibility, such as the time of day and the presence of fog, snow, rain, etc.

[0031] The vehicle sensor 24 is located on the ADAS-equipped vehicle 14 and captures biometric data of the driver 16 and vehicle data. The collected biometric data enables the ADAS-equipped vehicle 14 to identify changes in the emotional state of the driver 16. By capturing the biometric data, the ADAS-equipped vehicle 14 can detect stressful events that lead to an increased risk of impact. The biometric data includes, but is not limited to, eye tracking, eye squinting, facial expression identification, body displacement sensor in the seat, heart rate monitor in a steering wheel 26, voice volume, voice inflection, etc. The vehicle data includes factors that influence driving behavior, such as vehicle load, trailer status, tire pressure 28, tire wear 28, tire temperature 28, whether the mounted tire 28 is a spare tire, etc.

[0032] Remote vehicles 30 communicate external data and telemetry data 32 via a transceiver 36 to the control unit 20 of the ADAS-equipped vehicle 14. The external data includes data that correlates with an increased risk of collision, including weather data, impact data, a driver's familiarity 16 with a location, etc. The telemetry data 32 includes, but is not limited to, information about collision events and near-collision events in relation to a map 38. Events are classified as collision events when the remote vehicles 30 have collided. Events are classified as near-collision events when the remote vehicles 30 trigger alarms or an automatic vehicle response indicating a near-collision (i.e., steering inputs exceeding a steering threshold, braking inputs exceeding a braking threshold, etc.).

[0033] The control unit 20 in the ADAS-equipped vehicle 14 calculates the response of the ADAS-equipped vehicle 14 and issues warnings to promote safety. The control unit 20 is a non-generalized electronic control device with a pre-programmed digital computer or processor 40, a memory 42, an input and output port 44, and a transceiver 36.

[0034] The processor 40 can be a custom or off-the-shelf processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors assigned to the controller 20, a microprocessor-based semiconductor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or, more generally, a device for executing instructions. The memory 42 is used to store data such as control logic, software applications, instructions, computer code, data, lookup tables, and so on. The memory 42 includes any type of medium accessible by a computer, such as read-only memory (ROM), random-access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of storage medium.A "non-volatile" computer-readable medium excludes wired, wireless, optical, or other communication links that carry volatile electrical or other signals. A non-volatile computer-readable medium includes media in which data can be permanently stored and media in which data can be stored and later overwritten, such as a rewritable optical disc or an erasable storage device. Computer code includes any type of program code, including source code, object code, and executable code. Processor 40 is configured to execute the code or instructions.

[0035] The input and output port 44 receives incoming data from the one or more sensors 18 and communicates the incoming data to the processor 40. The input and output port 44 also receives outgoing data from the processor 40 and communicates outgoing data to the environmental sensor 22 and the vehicle sensor 24. In addition, the input and output port 44 is configured to communicate wirelessly with the one or more sensors 18 via the transceiver 36.

[0036] The transceiver 36 is configured to wirelessly communicate information to and from remote vehicles 30 and a cloud 46, such as other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems in a remote call center (e.g., GENERAL MOTORS’ ON-STAR), and / or personal devices. The ADAS-equipped vehicle 14 may include one or more antennas and / or transceivers 36 for receiving and / or transmitting signals, such as cooperative sensing messages (CSMs). The transceiver 36 can be considered a sensor.

[0037] Cloud 46 stores the telemetry data 32, the map 38, and a driver profile 48. The remote vehicles 30 communicate the telemetry data 32 and the map 38 to Cloud 46, while the ADAS-equipped vehicle 14 communicates the driver profile 48 to Cloud 46. The calculation of the driver profile 48 is described in more detail below.

[0038] With reference to Fig. Figure 2 shows a flowchart of a procedure 100 for adapting the reactions of the ADAS-equipped vehicle 14 based on a calculated increased risk of vehicle crash index (IRVCI). The procedure 100 begins with step 102, where the system 10 collects the telemetry data 32 communicated by the cloud 46 and the remote vehicles 30 via the transceiver 36. The procedure 100 then proceeds to step 104, where environmental data is collected. The procedure 100 then proceeds to step 106.

[0039] In step 106, impact risk factors are identified. To determine these factors, the telemetry data 32 and the environmental data are received by the controller 20 and processed by the processor 40. The processor 40 analyzes the telemetry data 32 in relation to the environmental data to identify factors that increase the impact risk. Impact risk factors are environmental data factors that correlate with impact and near-miss events. For example, impact risk factors can include road curvature, traffic patterns, intersection configurations, and so on. The procedure then proceeds to step 108.

[0040] In step 108, the areas with increased impact risk are determined. These areas are identified by pinpointing where the impact risk factors occur relative to map 38. Procedure 100 then proceeds to step 110.

[0041] In step 110, vehicle data is collected. The vehicle data includes factors relating to the ADAS-equipped vehicle 14 and influencing the driver's 16 driving behavior. This vehicle data includes vehicle load, trailer status, tire pressure 28, tire wear 28, tire temperature 28, and whether the fitted tire 28 is a spare tire, etc. The procedure 100 then proceeds to step 112.

[0042] In step 112, processor 40 determines the location of the ADAS-equipped vehicle 14. The location of the ADAS-equipped vehicle 14 is determined based on its position relative to map 38 using GPS. Procedure 100 then proceeds to step 114.

[0043] In step 114, the proximity of the ADAS-equipped vehicle 14 to the increased impact risk is determined by comparing the areas of increased impact risk with the location of the ADAS-equipped vehicle 14. If the location of the ADAS-equipped vehicle 14 is within an area of ​​increased impact risk, the procedure 100 proceeds to step 116. If the ADAS-equipped vehicle 14 is not within an area of ​​increased impact risk, the procedure 100 returns to step 112. In another embodiment, if the ADAS-equipped vehicle 14 is within a threshold range of the determined areas of increased impact risk, it is determined that the ADAS-equipped vehicle 14 is near the increased impact risk, and the procedure proceeds to step 116.If the ADAS-equipped vehicle 14 is not within the threshold range of the specified areas with increased impact risk, procedure 100 returns to step 112.

[0044] In step 116, the IRVCI is calculated. The IRVCI is calculated based on the proximity of the ADAS-equipped vehicle 14 to the identified increased impact risk and the collected vehicle data. Procedure 100 then proceeds to step 118.

[0045] In step 118, the ADAS 12 adjusts the response of the ADAS-equipped vehicle 14 based on the calculated IRVCI. For example, if the IRVCI is calculated and it is determined that the ADAS-equipped vehicle 14 is near a high-risk environment, the ADAS generates earlier warnings and responses. However, if the IRVCI is calculated and it is determined that the ADAS-equipped vehicle is not near a high-risk environment, the ADAS generates later warnings and responses. For example, if it is determined that the ADAS-equipped vehicle 14 is near a high-risk environment (i.e., high rainfall rate, low evasive maneuver potential, heavy traffic congestion, etc.), the ADAS-equipped vehicle 14 experiences responses from the ADAS 12, such as collision avoidance system (CAS) and / or automatic emergency braking (AEB).

[0046] With reference to Fig. Figure 3 illustrates a flowchart of Procedure 200 for adapting the responses of the ADAS 12 based on a calculated driver's predicted aggressiveness metric (D-PAM). Procedure 200 begins with step 202, where the ADAS-equipped vehicle 14 identifies the driving habits of the driver 16. The ADAS 12 recognizes the vehicle inputs common to the driver 16 over a period of time. The ADAS 12 classifies these common vehicle inputs as the driver's driving habits. These driving habits include vehicle speed, vehicle acceleration, vehicle deceleration, and steering inputs over a period of time. Procedure 200 then proceeds to step 204.

[0047] In step 204, the driving preferences of driver 16 are identified. Driving preferences are settings that driver 16 has entered into the ADAS 12. In a non-restrictive example, driving preferences include preferred timing for ADAS 12 warnings and responses, driving aggressiveness, preferred route information, etc. Procedure 200 proceeds to step 206.

[0048] In step 206, biometric data of the driver 16 is collected. The ADAS 12 collects this biometric data over a period of time using the vehicle sensor 24. The ADAS 12 detects when the vehicle inputs vary depending on the biometric data. Examples of biometric data include eye tracking, eye squinting, facial expression identification, body displacement sensor in the seat, heart rate monitor in the steering wheel 26, voice volume, voice inflection, etc. The procedure 200 then proceeds to step 208.

[0049] In step 208, driver profile 48 is generated for driver 16 of the ADAS-equipped vehicle 14. Driver profile 48 includes driver 16's driving habits, driving preferences, and biometric data. Procedure 200 then proceeds to step 210.

[0050] In step 210, driver profile 48 is used to calculate a driver's historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing driver profile 48 with a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for driver 16 based on driver profile 48. Procedure 200 then proceeds to step 212.

[0051] In step 212, vehicle data is collected. This includes vehicle load, trailer status, tire pressure, and tire wear. Procedure 200 then proceeds to step 214. In step 214, environmental data is collected. This includes road conditions, precipitation, and road visibility. Road conditions include road curvature, gradient, and surface finish. Procedure 200 then proceeds to step 216.

[0052] In step 216, the ADAS 12 monitors the vehicle inputs in real time. These inputs include vehicle speed, acceleration, deceleration, and steering inputs, all collected in real time. Procedure 200 then proceeds to step 218.

[0053] In step 218, the calculated D-HAM is compared with the vehicle inputs. The vehicle inputs may differ from the D-HAM. If the vehicle inputs fall within a certain range outside the calculated D-HAM, a deviation is detected. If the vehicle inputs deviate from the D-HAM, the procedure proceeds to step 220. If no deviation from the D-HAM is detected, the procedure returns to step 216, where the vehicle inputs continue to be monitored.

[0054] In step 220, impact risk factors are identified. These factors are identified based on vehicle and environmental data. They are used to determine the cause of the vehicle input's deviation from the D-HAM. Examples of impact risk factors include road curvature, traffic patterns, intersection configurations, and so on. Procedure 200 then proceeds to step 222.

[0055] In step 222, a predicted aggressiveness metric (D-PAM) of the driver is calculated. The D-PAM is calculated based on the identified impact risk factors. The D-PAM predicts the aggressiveness of the driver and the vehicle inputs under the identified impact risk factors. Procedure 200 then proceeds to step 224.

[0056] In step 224, the response of the ADAS-equipped vehicle 14 is adjusted based on the calculated D-PAM. The D-PAM predicts the driver's aggressiveness 16 under various impact risk factors. Using this prediction, the ADAS 12 adjusts its responses to enable the ADAS-equipped vehicle 14 to maintain safety and reduce the number of undesirable responses from the ADAS 12. For example, less aggressive drivers 16 receive earlier warnings and earlier, gentler vehicle responses. Conversely, more aggressive drivers 16 receive later warnings and later, less gentle vehicle responses.

[0057] System 10 and procedures 100 and 200 for adapting the response of the ADAS 12 described herein offer several advantages. These include generating an adapted response of the ADAS-equipped vehicle 14 based on environmental factors and the driver profile 48. Therefore, the ADAS 12 maintains safety while adapting its responses to the environment and the driver 16.

[0058] The description provided here is merely exemplary, and variations that do not deviate from the core of the description are to be considered within its scope. Such variations should not be regarded as a departure from the spirit and scope of the description provided.

Claims

[1] Method for adjusting a response of a vehicle equipped with an advanced driver assistance system (ADAS), the method comprising: Collecting telemetry data from a large number of remote vehicles, where the telemetry data includes impact events and near-impact events in relation to locations on a map; Collecting environmental data of the locations on the map, where the environmental data displays inherent properties of the locations, including road curvature, road gradient, road surface, visibility and weather conditions; Conducting an analysis of telemetry data in relation to the environmental data of the locations to determine impact risk factors that correlate with an increased impact risk; Identifying areas with increased impact risk based on impact risk factors; Determining the location of the ADAS-equipped vehicle in relation to the map; Collecting vehicle data from one or more sensors mounted on the ADAS-equipped vehicle, wherein the vehicle data includes vehicle load, trailer status, tire pressure, tire wear, tire temperature and fitted spare tires; Calculating an increased risk of a vehicle impact index (IRVCI) based on the location of the ADAS-equipped vehicle in relation to areas of increased impact risk and the vehicle data; and Adjusting the response of the ADAS-equipped vehicle based on the IRVCI. [2] The method of claim 1, further comprising collecting telemetry data from a cloud. [3] Method according to claim 1, further comprising classifying the telemetry data as the impact events when distant vehicles collide. [4] Method according to claim 1, further comprising classifying the telemetry data as the near-impact events when the remote vehicles activate alarms indicating a near-impact. [5] Method according to claim 1, further comprising classifying the telemetry data as the near-collision events when the remote vehicles activate automatic vehicle reactions indicating a near-collision. [6] Method according to claim 1, wherein the impact risk factors include road curvature, traffic patterns and intersection configurations. [7] Method according to claim 1, further comprising locating the impact risk factors relative to the map. [8] Method according to claim 1, further comprising adjusting the reaction time control of the vehicle equipped with ADAS. [9] Method according to claim 1, further comprising adjusting the reaction aggressiveness of the ADAS-equipped vehicle. [10] Method for adjusting a response of a vehicle equipped with an advanced driver assistance system (ADAS), the method comprising: Generating a driver profile, wherein the driver profile is based on collected driving habits and collected driving preferences, the driver's driving habits including vehicle speed, vehicle acceleration, vehicle deceleration and steering inputs over a period of time; Calculating a historical aggressiveness metric (D-HAM) of the driver by comparing driving habits with a statistical mean; Collecting vehicle data from one or more sensors mounted on the ADAS-equipped vehicle, wherein the vehicle data includes vehicle load, trailer status, tire pressure, tire wear, tire temperature and fitted spare tires; Collecting environmental data of locations on a map, where the environmental data displays inherent properties of the locations, including road curvature, road gradient, road surface, visibility, and weather conditions; Collecting real-time vehicle inputs from the driver, including speed, acceleration, deceleration, and steering inputs in relation to the map; Performing an analysis of vehicle and environmental data to determine impact risk factors when vehicle inputs deviate from the D-HAM; Calculating a predicted driver aggressiveness metric (D-PAM) from the impact risk factors based on the deviation of the D-HAM; and Adjusting the response of the ADAS-equipped vehicle based on the D-PAM.

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