Collision mitigation and avoidance

Abstract

A system comprising: a computer having a processor and memory, the memory storing instructions executable by the computer to: identify a target object; identify a potential intersection of a host vehicle and the target object; identify one or more data collection devices to provide data for analyzing the intersection; collect data relating to the host vehicle and the target object from the data collection devices; develop a coordinate map of a surrounding environment at least partially based on the data collected from the host vehicle and the target object; identify a probable trajectory of the target object at least partially based on the coordinate map; generate a hazard assessment at least partially based on the probable trajectory;and to activate at least one of several vehicle safety systems, at least partially, based on a hazard assessment obtained by analyzing the map.

Description

CROSS-REFERENCE TO PREVIOUSLY SUBMITTED APPLICATION This application claims priority over the preliminary US application, serial no. 62 / 173,796, filed on June 10, 2015, which is hereby incorporated in its entirety by reference. BACKGROUND Vehicle collisions often occur at intersections. Collision mitigation can be difficult and expensive to implement. Current systems are often unable to make good or any use of different types of data. Furthermore, current collision mitigation systems lack the ability to selectively use different data. German patent application DE 10 2011 086 402 A1 discloses a method and a driver assistance system for detecting a vehicle's surroundings. To obtain sufficiently plausible information about an object in a vehicle's surroundings, the method involves detecting the vehicle's surroundings with a sensor and, if an object is detected in the detected environment, detecting the object with an ultrasound system. German patent application DE 10 2014 219 742 A1 discloses a vehicle system and a method for obstacle detection and collision avoidance in an autonomous mode. The vehicle system comprises a sensor for detecting a target vehicle, a communication device for receiving infrastructure information, such as from traffic lights or road signs, and a processing device that controls the operation of at least one vehicle subsystem, such as a steering system or a braking system, depending on the infrastructure information. German patent application DE 10 2013 100 206 A1 discloses a method for collision avoidance between a carrier vehicle and a target vehicle, in which the intersection of the vehicles' paths of travel is defined as a potential collision zone. This method takes into account not only the distance and time required to reach and clear the collision zone, but also a corrected value for cases where the target vehicle is towing a trailer. Based on this information, a hazard number is determined, and warning signals are provided to the user to adjust the speed of the carrier vehicle. German patent application DE 10 2009 029 847 A1 discloses a safety system for a vehicle with an object sensor that detects the distance to an object as well as its speed and acceleration. Based on the vehicle's speed and acceleration, a control system determines a first time interval before the vehicle must begin braking and a second time interval before the vehicle must begin steering in order to avoid a collision with the object. The publication DE 10 2012 025 364 A1 discloses a method for displaying a warning relating to a wildlife crossing to the driver of a motor vehicle and a wildlife warning system. The publication EP 1 470 977 A1 discloses a parking and / or driving aid for a motor vehicle, which has at least one distance sensor that can be attached to the motor vehicle for determining at least one distance value between the motor vehicle and at least one obstacle. Based on this prior art, a system for collision mitigation and avoidance with the features of independent claim 1 and a method with the features of independent claim 11 are created. Advantageous embodiments can be found in the dependent claims. DRAWINGS Fig. 1 is a graphical system representation of a collision mitigation system. Fig. 2 illustrates a process flow for the system according to Fig. 1. Fig. 3 illustrates another process flow for the system according to Fig. 1. Fig. 4 illustrates a potential collision between a host vehicle and a target object. Fig. 5 illustrates a trajectory of the host vehicle. DESCRIPTION Fig. 1 illustrates a system 100 for intersection detection and collision mitigation. Unless otherwise specified in this disclosure, an “intersection” is defined as a location where the current or potential future trajectories of two or more vehicles intersect. Consequently, an intersection could be any location on a surface where two or more vehicles could collide, e.g., a road, a driveway, a parking lot, an access road to a public road, lanes, etc. Accordingly, an intersection is determined by identifying a location where two or more vehicles can meet, i.e., collide. Such a determination uses potential future trajectories of both a host vehicle 101 and other vehicles and / or other objects in the vicinity.As described in more detail below, using a selected or “scaled” set of data collection facilities, 110 future trajectories can be determined for one or more traffic scenarios involving a host vehicle 101 and / or a destination vehicle, e.g., where the destination vehicle turns during a traffic scenario, where a destination vehicle approaches the host vehicle 101, etc. System 100 comprises a vehicle 101, which in turn contains several data collection devices 105 and a computer device 110. The vehicle 101 may also include several vehicle safety systems 112, such as a brake assist system 115, a warning system 120, a steering assist system 125, a torque assist system 130, a passive safety system 135, and a headlight system 140. The vehicle 101 may also include a data storage device 145. System 100 includes a network 170, which has a data storage device 175, and a remote location 180, which has a data storage device 185. Network 170 can be used for communication between the vehicle computer device 110 and the remote location 180, or between multiple vehicles 101. The remote location 180 can contain a social media platform, a location providing navigation information, environmental information, etc. The computer device 110 comprises a processor and a memory, the memory comprising one or more forms of computer-readable media, such as volatile and / or non-volatile memory, as known, and storing instructions that can be executed by the processor to perform various operations, including those disclosed herein. Furthermore, the computer device 110 may include more than one computer device, such as a controller or the like, contained in the vehicle 101, for monitoring and / or controlling various vehicle components, such as an engine control unit (ECU), a transmission control unit (TCU), etc. The computer device 110 is generally configured for communication via a network within the vehicle and / or a communication bus, such as a CAN bus or the like.The computer device 110 may also have a connection to an on-board diagnostic connector (OBD-II). Via the CAN bus, the OBD-II, and / or other wired or wireless mechanisms, the computer 105 can send messages to various devices in a vehicle and / or receive messages from the various devices, e.g., controllers, actuators, sensors, etc., including the data collection devices 105. Alternatively or additionally, in cases where the computer 105 actually comprises multiple devices, the CAN bus or the like may be used for communication between the devices, which are described in this disclosure as the computer device 110. Additionally, the computer device 110 can be configured to communicate with the network 170, which, as described below, may include various wired and / or wireless networking technologies, such as cellular, Bluetooth, wired and / or wireless packet networks, etc. Furthermore, the computer device 110 generally includes instructions for receiving data, e.g., from one or more data collection devices 105 and / or a human-machine interface (HMI), such as an interactive voice response (IVR) system, a graphical user interface (GUI) including a touchscreen, or the like, etc. The computer device 110 can control various components and / or operations of the vehicle 101 using data received by the computer device 110, e.g., from the data collection devices 105, data contained as stored parameters, the server, etc. The computer device 110 can, for example, be used to control the speed, acceleration, deceleration, steering, etc., of the vehicle 101. The data collection devices 105 may include front data collection devices 150 in the front of the vehicle 101, rear data collection devices 155 in the rear of the vehicle 101, left collection devices 160 on the left side of the vehicle 101, and right collection devices 165 on the right side of the vehicle 101. The data collection devices 105 may include radar and / or lidar and / or CMOS / CCD cameras and / or vehicle-to-vehicle communication (including, but not limited to, dedicated short-range communication and cell communication) and / or a global positioning system (GPS) and / or ultrasound. The data collection devices 105 can include various devices. For example, different controllers in a vehicle can act as the data collection devices 105 to provide data via the CAN bus, such as data regarding vehicle speed, vehicle acceleration, etc. Furthermore, sensors or the like, global positioning system (GPS) devices, etc., could be included in a vehicle and configured as data collection devices 105 to provide data directly to the computer device 110, for example, via a wired or wireless connection. The data collection devices 105 could also include sensors or the like for detecting conditions outside the vehicle 101, such as medium-range and long-range sensors. The sensor data collection devices 105 could include mechanisms such as...The data collection devices 105 may include radar, lidar, sonar, cameras, or other image acquisition devices that could be used to measure the distance between the vehicle 101 and other vehicles or objects, to detect other vehicles or objects, and / or to detect road conditions such as curves, potholes, dips, bumps, changes in gradient, etc. Additionally, the data collection devices 105 may include sensors within the vehicle 101, such as accelerometers, temperature sensors, motion detectors, etc., to detect the movement or other conditions of the vehicle 101. A memory of the computer device 110 generally stores the collected data. The collected data can include various data collected in a vehicle 101 by the data collection devices 105. Examples of the collected data are provided above, and the data can also include additional data calculated from it in the computer device 110. In general, the collected data can include any data that can be collected by the data collection devices 105 and / or calculated from such data. Accordingly, the collected data could include various data relating to the operations and / or performance of the vehicle 101, as well as data specifically relating to the movement of the vehicle 101. The collected data could, for example, include data relating to speed, acceleration, longitudinal motion, lateral motion, pitch, yaw, roll, braking, etc.of vehicle 101. A memory component of the computer device 110 can also store one or more parameters. A parameter generally controls the use of the collected data. For example, a parameter can provide a threshold against which the calculated collected data can be compared to determine whether a setting should be applied to the component. Similarly, a parameter could provide a threshold below which an element of the collected data, such as a data element from an accelerometer, should be discarded. The components of vehicle 101 can include various elements of vehicle 101. As mentioned above, a component can be a video screen, a seat, an air conditioner, an interior or exterior mirror of vehicle 101, etc. The System 100 can be selectively scalable depending on the number of data acquisition devices 105 it contains. Specifically, the System 100 can contain and selectively activate one, some, or all of the following exemplary data acquisition devices 105 and / or receive and / or use data from one, some, or all of the following exemplary data acquisition devices 105: a forward-facing radar (long-, medium-, and short-range), a forward- / side-facing scanning lidar, a forward-facing CMOS (complementary metal-oxide-semiconductor) / CCD (charge-coupled device) camera (monocular or stereoscopic, with and without active illumination FIR capability), a forward-facing ultrasonic (long-, medium-, and short-range), a forward right / left radar (long-, medium-, and short-range), a forward right / left ultrasonic (long-, medium-, and short-range).a side-facing CMOS / CCD camera mounted in the center of the vehicle, a rear right / left radar (long, medium, and short range), a rear right / left ultrasonic sensor (long, medium, and short range), a rear-facing radar (long, medium, and short range), a rear-facing scanning lidar, rear-facing ultrasonic sensors (long, medium, and short range), a rear-facing CMOS / CCD camera (with and without active illumination FIR capability), vehicle-to-vehicle communication, e.g., DSRC (dedicated short-range communication), cell-based communication, etc., a global positioning system (GPS), and an electronic map of the road / surroundings based on a GPS location. The computer 110 can be programmed to receive and merge (or integrate) the data collected by some or all of the various data collection devices 105 contained in a vehicle 101 in a scalable manner. As the “scalable” use or merging of the collected data is used here, it means that the computer 105 is programmed to perform collision detection partly by identifying specific data collection devices 105 for a particular condition, such as whether a collision is imminent or probable. The system 100 being “scalable” further means that the data collection devices 105, and consequently the data they collect, can be added to and / or removed from a set of data collection devices 105 used from a first determination to a second determination of whether a collision is detected (and so on).The computer 110 is generally programmed to make scaling decisions, i.e., to perform a determination of which data collection facilities 105 in the system 100 are to be used as sources for the collected data, which are to be used in a special determination of whether a collision is possible. The front radar, front camera, and front lidar sensors can detect, for example, turn-over-the-path scenarios when the target vehicle turns in an arc into the forward trajectory of the host vehicle. However, for a path-crossing scenario, the side radar and side lidar are required in addition to the front radar, front camera, and front lidar. Consequently, depending on whether computer 110 detects a turn over the path or a path crossing, selected data collection devices 105 can be activated. In another example, a front-mounted wide-angle camera can assist in vehicle length estimation for turn-across-the-path, cross-the-path, and brake-to-pass scenarios. Forward-facing radar and / or lidar could also be used. A brake-to-pass scenario occurs when the host vehicle accelerates or decelerates to avoid the target vehicle. Vehicle-to-vehicle communication enables scanning of an obscured field of view, such as when a second target vehicle is in front of the target vehicle and out of the host vehicle's sight. An electronic horizon and GPS can provide a significant amount of infrastructure for mapping, such as stop lines, intersection types, lane types, paths, etc. Vehicle-to-infrastructure communication enables signal, phase, and timing scans for traffic lights, including the period of a stop light.A camera can also see the signal status of the traffic light, such as whether it is green, yellow or red. The computer 110 is further programmed to use the data collected by the data collection devices 105, such as data about the environment around a vehicle 101, and data available from a CAN bus regarding the vehicle 101's speed, steering angle, acceleration, etc., to generate a three-dimensional digital road network map that provides a complete Cartesian XYZ road network coordinate system. This three-dimensional map offers significant advantages over a conventional tracking and target selection system using polar coordinates. For example, using the Cartesian XYZ coordinates, the system 100 can determine the lateral speed and acceleration of vehicles at an intersection of vehicle paths. Returning to Fig. 1, the network 170 represents one or more mechanisms by which the computer device 110 can communicate with a network data storage device 175 and / or a remote location 180 having a data storage device 185. Accordingly, the network 170 can be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and / or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies if multiple communication mechanisms are used). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth, IEEE 802.11, etc.), local area networks (LANs), and / or wide area networks (WANs), including the Internet, providing data communication services. System 100 can determine the trajectories of a host vehicle 101 and a target object, e.g., a target vehicle 101, to determine whether an intersection and a potential collision between the host vehicle 101 and the target object can occur. Using the data from the data collection devices 105, the computer 110 can determine predicted trajectories for the host vehicle 101 and the target object. Computer 110 can determine the predicted paths for the vertices of host vehicle 101. Host vehicle 101 has four vertices: a front right vertex, a front left vertex, a rear right vertex, and a rear left vertex. For the purposes of the following equations, "front" refers to the foremost end of host vehicle 101, "rear" refers to the rearmost end of host vehicle 101, "right" refers to the right side of the vehicle when viewed from the front end, and "left" refers to the left side of the vehicle when viewed from the front end. Furthermore, this discussion refers to two directional axes: an "X" axis, defined as a line representing the shortest distance between the left and right sides of a road, and a "Y" axis, defined as the axis extending in one direction along the road, e.g.,parallel to a road that is essentially straight, or tangent to a road whose direction is defined by a curve, the Y-axis being orthogonal to the X-axis. When vehicle 101 travels along a road, the forward motion of the car defines the positive Y-axis, with the X-axis pointing to the right with respect to the positive Y-direction. The host vehicle 101 can define a coordinate system with an origin at the front left corner of the host vehicle 101, with the positive X pointing to the right with respect to the front left corner and the positive Y pointing forward with respect to the front left corner. The trajectory of vehicle 101 can define an angle ψ with respect to the X-axis, as shown in Fig. 5, with the positive ψ traveling counterclockwise.While the discussion here focuses on the two axes X and Y, the data collection facilities 105 can be located at specific heights along a Z-axis that is orthogonal to the X and Y axes, and can be used to develop a two-dimensional map using the XY axes and / or a three-dimensional map using the Cartesian XYZ axes. The host vehicle 101 and the target vehicle 101 can define an intersection zone, as shown in Fig. 4. The intersection zone is defined as the potential space at the intersection where the host vehicle 101 can collide with the target vehicle 101, i.e., the area where the path of the host vehicle 101 can cross the path of the target vehicle 101. The data collection devices 105 can provide data specifying the dimensions of the host vehicle 101 with respect to the coordinate system described above. Specifically, the width wh of the host vehicle 101 can be determined, where wh is the distance from the front left point to the front right point along the X-axis. Furthermore, the length lh of the host vehicle 101 can be determined, where lh is the distance from the front left point to the rear left point along the Y-axis. One of the data collection devices 105 can be a sensor for determining the paths of the vertices, where the position of the sensor in the vehicle 101 can be defined as where the position of the sensor along the X-axis is and the position of the sensor along the Y-axis is .The distance from the sensor to the front end La, the rear end Lb, the right side Lc and the left side Ld can be defined as follows: where the four corners can each be defined as the point where an end meets one of the sides: the front left corner ad, the front right corner ac, the rear left corner bd and the rear right corner bc. At a given time t, a given time step index j, and a time step, i.e., a period, ΔT, such that from time t to time t + T, the elapsed time T is divided into equal steps ΔT such that Σ ΔT = T, the data collection device 105 can determine the trajectory of the host vehicle 101 by, where X̂h is the predicted position vector of the vehicle 101 along the X-axis, Ŷh is the predicted position vector of the vehicle 101 along the Y-axis, and ψ̂h is the predicted angle with respect to the X-axis defined by the predicted trajectory of the vehicle 101. Where, and the predicted location of the four vertices can be determined by the following: The index j increases by 1, calculating the next step in the path until a final time T is reached, i.e., when . Consequently, the predicted path rh for the four corners ad, ac, bd, bc of vehicle 101 can be determined as a matrix containing each time step: The computer device 110 can determine the corner of the host vehicle 101 that is closest to the target vehicle 101. Like the host vehicle 101, the target vehicle has a width wtg, a length ltg, and the position vectors X̂tg, Ŷtg, ψ̂̂̂tg. The target vehicle 101 can define an azimuth angle θtg, which is defined as the angle between the position of the host vehicle 101 and the target vehicle 101 with respect to the Y-axis, where the positive θtg runs counterclockwise, i.e., when the host vehicle 101 sees the target vehicle 101 to its left, the azimuth angle θtg is positive. If the azimuth angle θtg(t) is negative at time t, i.e., θtg(t) < 0, the target vehicle 101 is on the right side of the host vehicle 101, and the computer device 110 can determine which corner of the target vehicle 101 is closest to the host vehicle 101. If ψ̂tg(t) -ψ̂h(t) ∈ [0°, 90°), then the distances for two possible corners d1, d2 can be defined by the following: If d1 < d2, then the rear left corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the front left corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If ψ̂tg(t) - ψ̂(t) ∈ [90°, 180°), then: If d1 < d2, then the front left corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the front right corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If ψ̂tg(t) - ψ̂h(t) ∈ [180°, 270°) holds, then the distances for two possible corners d1, d2 can be defined by the following: If d1 < d2, then the front right corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the rear right corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If ψ̂tg(t) - ψ̂h(t) ∈ [270°, 360°) holds, then: If d1 < d2, then the rear right corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the rear left corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If the azimuth angle θtg(t) is positive at time t, i.e., θtg(t) ≥ 0, the target vehicle 101 is on the left side of the host vehicle 101, and the computer device 110 can determine which corner of the target vehicle 101 is closest to the host vehicle 101. If ψ̂tg(t) - ψ̂h(t) ∈ [0°, 90°), then the distances for two possible corners d1, d2 can be defined by the following: If d1 < d2, then the rear right corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the rear left corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If ψ̂tg(t) -ψ̂h(t) ∈ [90°,180°) holds, then: If d1 < d2, then the rear left corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the front left corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If ψ̂tg(t) - ψ̂h(t) ∈ [180°, 270°) holds, then the distances for two possible corners d1, d2 can be defined by the following: If d1 < d2, then the front left corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the front right corner of the target vehicle 101 is the nearest corner on the host vehicle 101. If ψ̂tg(t) - ψ̂h(t) ∈ [270°, 360°) holds, then: If d1 < d2, then the front right corner of the target vehicle 101 is the nearest corner on the host vehicle 101; otherwise, the rear right corner of the target vehicle 101 is the nearest corner on the host vehicle 101. Based on which corner of the target vehicle 101 is closest to the host vehicle 101, the computer device 110 can determine a detection corner value Dcorner(t) that refers to each corner, where: The computer device 110 can then determine the predicted path of the target vehicle 101. The computer device 110 can acquire the width wtg and the length ltg of the target vehicle from the data collection devices 105. For each time step index j, as described above, the computer device 110 can determine the corner of the target vehicle 101 that is closest to the host vehicle 101. The computer device 110 determines the trajectory of the target vehicle as follows: where X̂tg is the predicted position vector of the target vehicle 101 along the X-axis, Ŷtg is the predicted position vector of the vehicle 101 along the Y-axis, and ψ̂tg is the predicted angle with respect to the X-axis defined by the predicted trajectory of the vehicle 101. Where the following holds: If Dcorner(t + jΔT) = 1, i.e., the nearest corner of the host vehicle 101 to the target vehicle 101 is the front left corner of the host vehicle 101, the trajectories of the corners of the target vehicle 101 can be determined by the following: If Dcorner(t + j ΔT) = 2 holds, i.e., the nearest corner of host vehicle 101 to target vehicle 101 is the front right corner of host vehicle 101, then If Dcorner(t + j Δ T) = 3, i.e., the nearest corner of host vehicle 101 to target vehicle 101 is the rear left corner of host vehicle 101, then If Dcorner(t + j ΔT) = 4, i.e., the nearest corner of host vehicle 101 to target vehicle 101 is the rear right corner of host vehicle 101, then For the period from t to t + T, the predicted path r of the corners of the target vehicle 101 can be determined by the following: Using the path values ​​r for the corners of the host vehicle 101 and the target vehicle 101, the computer device 110 can determine whether the host vehicle 101 will collide with the target vehicle 101. For the purposes of the following equations, the computer device 110 can use a predetermined collision distance threshold dth = vmaxΔT, where vmax is a predetermined speed threshold. The collision distance threshold dth defines the distance beyond which the computer device 110 determines that a potential collision is not imminent. The computer device 110 can also use a predefined maximum time constant TTmax, e.g., 100 seconds. The maximum time constant TTmax defines a maximum time that the host vehicle 101 and / or the target vehicle 101 may require to reach and / or clear the intersection zone. The following equations use the corner indices λ, µ to refer to the corners of the host vehicle 101 (λ) and the target vehicle (µ). The indices λ, µ can be one of 1, 2, 3, or 4, corresponding to the detection corner value Dcorner: That is, λ = 1 refers to the front left corner of the host vehicle 101, µ = 3 refers to the rear left corner of the target vehicle 101, etc. For each step index, the corners λ, µ of the host vehicle 101 and the target vehicle 101 can be separated by a distance d*, where one of the distances d*(j, λ, µ) is a minimum distance, i.e., the closest distance the host vehicle 101 will be to the target vehicle 101. Let k be a step index such that , similar to j, but increasing independently of j. The index k where the minimum distance occurs can be assigned an index k* by the following: Using the arrangement of the minimum distances d*, an absolute minimum distance from the host path to the target path d** between any two corners λ, µ and the index of this point j** can be determined as the minimum of d*: With the minimum distance d**, the computer device 110 can calculate whether the host vehicle 101 will potentially collide with the target vehicle 101 and / or whether the host vehicle 101 or the target vehicle 101 will clear the crossing zone. If d**(λ,µ) > dth, i.e., if the shortest distance between the host vehicle 101 and the target vehicle 101 is greater than a threshold distance, then there is probably no danger of a collision between the host vehicle 101 and the target vehicle 101, where the crossing marker Fcrossing and the values ​​of the time to vehicle TTVh, TTVtg, i.e., the time until the host vehicle 101 reaches the target vehicle 101 (TTVh) and the time until the target vehicle 101 reaches the host vehicle 101 (TTVtg), are reset to the predefined constants. If d**(λ, µ) ≤ dth for any pair of vertices λ, µ, then the crossing marker is set to 1, where the time period until the vehicle is determined based on the indices j**, k* determined above: The path crossing indicator Fcrossing(t) is set to the minimum value of Fcrossing for all pairs of corners λ, µ: The computer device 110 can then calculate the time until the crossing zone TTRe is reached and the time until the crossing zone is cleared TTCI for the host vehicle 101 and the target vehicle 101 at a time t. If Fcrossing(t) = 0, i.e., there is likely no potential for a collision, then the times until reaching and clearing are set to a predetermined constant: If Fcrossing(t) = 1, then a collision between the host vehicle 101 and the target vehicle 101 is possible. To determine whether the path of the host vehicle 101 will cross the path of the target vehicle 101, the computer device can determine the time until the host vehicle 101 reaches the path of the target vehicle 101, TTRe, and the corners (λRe,h, μRe,h) that can satisfy the following: The time until the host vehicle 101 clears the path of the target vehicle 101, TTRe and the corresponding corners (λCl,h, µCl,h) can be determined by the following: The time until the target vehicle 101 reaches the host vehicle 101, TTRetg, the time until the target vehicle 101 clears the path of the host vehicle 101, TTCltg, and the corresponding corner pairs can be determined similarly: Using all these values, the computer device 110 can then determine a hazard assessment for the host vehicle 101 and the target vehicle 101. Specifically, the computer device 110 can determine the acceleration hazard number ATN, the braking hazard number BTN, and the steering hazard number STN for the host vehicle 101 and the target vehicle 101, whereby, based on the hazard numbers ATN, BTN, and STN, which can be combined into a single hazard number TN, the vehicle 101 initiates countermeasures. The braking hazard number (BTN) can be calculated as two separate values: one representing the braking hazard number for the host vehicle 101 braking to allow the target vehicle 101 to pass (BTNp), and the other representing the braking hazard number for braking to a complete stop (BTNs). Depending on the trajectory of the target vehicle 101, the braking hazard numbers for stopping the host vehicle 101 and slowing down to allow the target vehicle 101 to pass can differ; that is, BTNp cannot always be equal to BTNs. The steering hazard number STN can be calculated as two separate values. One represents the steering hazard number for steering the host vehicle 101 to clear the intersection zone before the target vehicle 101 reaches the intersection zone (i.e., turning around), STNo. The other represents the steering hazard number for steering the host vehicle 101 to allow the target vehicle 101 to clear the intersection zone first (i.e., counter-steering), STNc. Depending on the trajectory of the target vehicle 101, the steering hazard numbers for turning around and counter-steering by the host vehicle 101 can be different; that is, STNo cannot always be equal to STNc. The computer device 110 can have a predefined maximum longitudinal acceleration value and a predefined maximum lateral acceleration value for the host vehicle 101 and the target vehicle 101. The computer device 110 can use a time step index K in the following equations. For a time tK, the computer device 110 can acquire the velocities of the host vehicle 101, vh(fK), and the target vehicle 101, vtg(tK), from the data collection devices 105. Using the equations above, the computer device 110 can then determine the path crossing marker Fcrossing(tK) and the values ​​TTRe, TTRCI of the time to reach and time to clear for time tK. If Fcrossing(tK) = 0, then there is likely no potential path crossing and consequently no potential hazard, with all hazard numbers set to 0. If the intersection marker Fcrossing(tK) = 1, then a potential intersection may exist. Computer device 110 compares the time period TTCltg until the target vehicle 101 clears the intersection zone with the time period TTReh until the host vehicle 101 reaches the intersection zone to determine whether the target vehicle 101 clears the intersection zone before the host vehicle 101 reaches it. Computer device 110 also compares the time period TTClh until the host vehicle 101 clears the intersection zone with the time period TTRetg until the target vehicle 101 reaches it to determine whether the host vehicle 101 clears the intersection zone before the target vehicle 101 reaches it. If the time until either of the vehicles 101 clears the intersection zone is shorter than the time until the other vehicle 101 reaches the intersection zone, i.e.Then one of the vehicles 101 clears the intersection zone before the other vehicle 101 reaches the intersection zone. Consequently, there is no potential road crossing and no potential hazard, with the hazard numbers remaining 0, as shown above. Otherwise, the computer device 110 can calculate the hazard numbers for the host vehicle 101 and the target vehicle 101. For clarity of the following equations, let The computer device 110 can then determine the acceleration hazard number ATN for the host vehicle 101. For the indices j = j1, j2, let j1 = J3, j2 = J2 - 1. The computer device 110 can calculate a characteristic distance D* and a characteristic acceleration A* for the host vehicle 101 in order to determine the acceleration hazard number ATN: The computer device 110 can determine the braking risk number BTNp for braking the host vehicle 101 to allow the target vehicle 101 to pass. Let the indices j1 = J1, j2 = J4 - 1. The computer device 110 can determine the braking hazard number BTNs for braking the host vehicle 101 in order to stop the host vehicle. Let the indices j1 = 0, λ = J1 - 1. The computer device 110 can determine the steering hazard number STNo for rerouting, i.e., steering the host vehicle 101 to clear the intersection zone before the target vehicle 101 reaches the intersection zone. Let the indices j1 = J3, j2 = J2 - 1. The computer device 110 can determine the steering hazard number STNc for counter-steering, i.e., steering the host vehicle 101 to allow the target vehicle to clear the intersection zone. Let the indices j1 = J1, j2 = J4 - 1. The computer device 110 can determine the acceleration hazard number ATNtg for accelerating the target vehicle 101 to pass the host vehicle 101. Let the indices j1 = J1, j2 = J4 - 1. The computer device 110 can determine the braking risk number BTNp,tg for braking the target vehicle 101 to allow the host vehicle 101 to pass the target vehicle 101. Let the indices j1 = J3, j2 = J2 - 1. The computer device 110 can determine the braking risk number BTNs,tg for braking the target vehicle 101 to a stop. Let the indices j1 = 0, j2 = j3 - 1. The computer device 110 can determine a steering hazard number STNo for rerouting, i.e., steering the target vehicle 101 to clear the intersection zone before the host vehicle 101 reaches the intersection zone. Let j1 = J1, j2 = J4 - 1. The computer device 110 can determine the steering hazard number STNc for counter-steering, i.e., steering the host vehicle 101 to allow the target vehicle to clear the intersection zone. Let the indices j1 = J3, j2 = J2 - 1. The computer device 110 can calculate the total hazard numbers TNh, TNtgals, and the minimum of the specific hazard numbers: The computer device 110 can determine the hazard assessment TN for all time points tK, where the following applies. Fig. 2 illustrates a process 200 for detecting a potential collision and mitigating the collision. The process 200 begins in a block 205 where the computer device 110 scales the collected data, i.e., identifies some or all of the available data collection devices 105 to be used for collecting the data for detecting potential intersections, as described with respect to this process 200. The computer device 110 may, for example, choose to select the left collection devices 160 but not the right collection devices 165, or may choose to use a specific type of collection device 105. For example, at night or during heavy rainfall, the camera collection devices 105 may not be useful, while the other collection devices 105 may be. In general, the selected data collection devices 105 may depend on various factors, e.g.,The trajectory of the target vehicle, the trajectory of the host vehicle, the surrounding environment, etc., can vary. Specifically, radar and lidar can be used in conjunction for longitudinal trajectory support. The lidar and camera can be used to estimate the transition from longitudinal to lateral motion, e.g., when turning, as the camera and lidar can detect the planes of the target vehicle, which may change shape during the crossing. Next, in a block 210, the computer 105 uses data from one or more data collection devices 105, selected in block 205, to identify a target object or objects, such as a target vehicle, a target guardrail on a roadway, an obstacle on a roadway, etc. For example, different data collection devices 110 can be used to determine that a target object, such as another vehicle, is within a predetermined distance, such as ten meters, twenty meters, etc., of the host vehicle 101. Furthermore, the data collection devices 105 could provide data indicating that the target object has approached the host vehicle 101, potentially changing its course, acceleration, deceleration, etc. Based on such data, the computer 110 could perform a preliminary determination that an object might cross the host vehicle 101. Next, the computer device 110 determines in block 215 whether there is a potential intersection between a host vehicle and the target object identified in block 210. If not, or if no target object was identified in block 210, the process 200 continues to block 220. If a potential intersection is identified, the process 200 continues to block 225. The identification of a potential intersection can be performed in various ways according to the data collected by one or more sensor data collection devices 105. For example, the approach speed above a certain threshold, acceleration or deceleration above a threshold, turning speed, etc., of a target vehicle can be used.Additionally, the identification can consider the period used for scanning or the period since the data collection facilities last collected data. Next, computer device 110 determines in block 220 whether process 200 should continue. If, for example, vehicle 101 stops, is switched off, or parked, process 200 can be terminated. If process 200 is to be continued, it proceeds to block 205. Otherwise, process 200 terminates. Next, the computer device 110 in block 225 merges or integrates the data collected by the data collection devices 105 selected in block 225. Such a merger or integration of the collected data provides situation-specific knowledge of the state of the host vehicle 101 with respect to a target vehicle and / or other potential target objects. Next, the computer device 110 generates a digital road network map in a block 230 based on the data merged by the data collection devices 105. In an exemplary implementation, the digital road network map is either a 2-dimensional Cartesian XY map or a 3-dimensional Cartesian XYZ map of an environment surrounding a vehicle 101, e.g., within a square or a rectangle having a center point at the vehicle's midpoint, with predetermined distances from the center, e.g., twenty meters, fifty meters, etc. As described in the equations above, the host vehicle 101 and the target vehicle 101 can move in an XY plane, with the data collection devices 105 positioned at different heights along a Z-axis orthogonal to the XY plane.The computer 110 can generate a two-dimensional XY map and / or a three-dimensional XYZ map to account for the movement of the vehicles 101 and the position of the data collection devices 105. The road network maps in Cartesian XY and XYZ coordinates advantageously allow for more accurate lateral target tracking and selection compared to conventional tracking and target selection systems in polar coordinates. The map is generated using both the merged data from the data collection devices 105, which identifies the location, shape, movement, etc., of one or more target objects relative to the vehicle 101, and information such as map data or navigation information stored in the computer 110's memory, received from a remote location, etc.Additionally, but not necessarily, a navigation system or similar device could be used to generate an event horizon, as is known, to provide data for the digital map. Depending on the number and / or quality of the data collection facilities 105 used in the data merging of block 220, the digital road network map may have a partial or a full field of view of the traffic and the environment surrounding the host vehicle. That is, the data with sufficient reliability may only be available for a partial view. Next, the computer device 110 determines the states of the host vehicle 101 and the target object, e.g., the target vehicle, in a block 235. The vehicle states typically include one or more velocity measurements, including lateral and longitudinal velocity, as well as acceleration, including lateral and longitudinal acceleration. The computer device 110 can use the digital road network map to determine the lateral velocity and lateral acceleration of the host vehicle and the target vehicle with respect to the coordinates on the map. Next, the computer device 110, in a block 240, generally determines a probability of driver intent for the target vehicle and the host vehicle using the merged data and the vehicle signals from the driver's controls. A probability of driver intent is the probability that a driver of a vehicle, such as a host vehicle 101, will follow or change a specific trajectory. Steering angle, throttle state, braking, and the like can all be used to estimate the probability that a driver will follow or change a currently planned trajectory. External factors, such as road conditions, weather, the type of road or driving surface (e.g., a parking lot), etc., can also be used. A probability of driver intent can be expressed as a percentage, i.e.,, a probability that a vehicle, such as a host vehicle 101, will remain on a current trajectory. Furthermore, the computer device 110 can track and estimate the probability of the intention of the driver of the host and the target vehicle on a continuous basis. Next, the computer device 110 in a block 245 determines a hazard estimate for an impending collision. The hazard estimate is a prediction of whether a target object will cross or collide with the host vehicle 101, based on the merged data from the data collection devices 105, the vehicle states, the probability of the host driver's intent, and / or the probability of the target driver's intent. Furthermore, the hazard estimate can include location-specific estimates. For example, the target vehicle may have a priority collision with the left side of vehicle 101, and the hazard estimate can include an estimate specific to the left side of vehicle 101. The hazard estimate can also include the traffic flow and lane configurations for the current road. Consequently, the hazard estimate can account for multiple traffic scenarios and potential intersections.The level of the scenarios can be proportional to both the amount and complexity of the sensor load and data fusion, as well as the probability of the intention of the driver of the target vehicle. The computer device 110 can calculate a steering danger number (STN), a braking danger number (BTN), and an acceleration danger number (ATN) for both the host vehicle and the target vehicle. As described above, the BTN is a measure of a change in longitudinal acceleration to allow either the host vehicle to stop or the object to pass the host vehicle; the STN is a measure of a change in lateral acceleration to allow either the host vehicle or the object to clear an intersection zone; and the ATN is a measure of a specific longitudinal acceleration to allow one of the host vehicle and the object to pass the other. That is, the BTN is a measure of the vehicle's longitudinal acceleration and can be determined using data from the data collection devices 105. The STN is a measure of the vehicle's lateral acceleration.The ATN is a measure of throttle valve changes. The computer device can use the STN, BTN, and / or ATN to determine the trajectories of the host vehicle and the target vehicle, and consequently generate the hazard assessment. The computer device 110 can also incorporate traffic flow and lane configurations on a roadway at an infrastructure-level intersection or at a point on a roadway where two or more vehicles have estimated trajectory intersection points, e.g., a business entrance, a ramp, etc., whereby it can adjust the sensor load and data fusion with a broader contextual understanding of the vehicle path intersection. The computer device 110 can further incorporate roadway traffic control mechanisms to determine the probability of the host and target drivers' intentions and to assess hazards.By combining traffic regulations with the probabilities of drivers' intentions, other vehicle trajectories sampled in the environment, and potential map data fused with the sampled data to provide a street-level fusion, the system can use 100 different hazard assessment techniques based on the varying behavior of ordinary crossing traffic. Next, the computer device 110 can implement countermeasures in a block 250 based on the hazard assessment, such as the safety systems 112, as described in more detail below with reference to Fig. 3. Next, in block 255, computer device 110 determines whether the host vehicle and the target vehicle remain on an intersecting path. This determination can be based on the hazard assessment; for example, if the hazard assessment is below a certain threshold, the intersection of the vehicle paths can end. If the host vehicle is still at the intersection, process 200 returns to block 225. If the host vehicle is no longer at the intersection, process 200 terminates. Fig. 3 shows a process 300 for implementing the countermeasures of block 250. The process 300 begins in a block 305, where the computer device 110 receives the hazard assessment determined in block 245 according to Fig. 2. Next, the data collection devices 105 in a block 310 determine the lateral and longitudinal acceleration of the host vehicle 101. The host vehicle's acceleration, in conjunction with the target vehicle hazard assessment, can further predict the impending collision and mitigation countermeasures. Based on the host vehicle 101's acceleration and the hazard assessment, the computer device 110 can reduce at least one of the host vehicle 101's lateral and longitudinal accelerations. For example, if the time to reach the intersection zone for the host vehicle 101 and the time to reach the intersection zone for the target vehicle 101 would result in a collision, the computer device 110 can slow down the host vehicle 101 so that the time to reach the intersection zone for the host vehicle 101 is greater than the time to clear the intersection zone for the target vehicle 101.This allows the target vehicle 101 to clear the intersection zone before the host vehicle 101 reaches the intersection zone, thus avoiding a collision. Next, based on the hazard assessment, computer device 110 in block 315 activates the warning system's countermeasures. Computer device 110 can use warning system 120 to deliver a warning to the host vehicle's driver. The warning can be any or all of the following: audible, visual, and / or haptic. The warning system's countermeasures can be suppressed if the hazard assessment would result in an increase. Next, based on the hazard assessment and the acceleration of the host vehicle, the computer device 110 activates the steering countermeasures in a block 320. Specifically, the computer device 110 can recommend to the driver of the host vehicle that the steering assistance system 125 be used to avoid the target vehicle. Alternatively, the computer device 110 can automatically activate the steering assistance system 125. The steering assistance system countermeasures can include steering support, a smaller or larger steering sensitivity for a given host steering wheel input, automated steering at different lateral acceleration levels at different times before the collision, and / or suppression of the level of changes in steering sensitivity.In particular, the hazard assessment can be dynamically adjusted based on the actions of the host vehicle, with the steering assistance system being able to adjust the steering assistance countermeasures to actively reduce the hazard assessment. Next, the computer device 110 in a block 325 activates the engine torque support countermeasures. Specifically, the computer device 110 can automatically activate the engine torque support system 130. The engine torque support system countermeasures can include increased or decreased longitudinal acceleration sensitivity for a given host accelerator pedal input at various times prior to the collision and / or suppression of any change in engine torque that would result in an increase in hazard estimation, such as the host vehicle causing a change in an impact condition from a frontal impact to a side impact. Next, the computer device 110 activates the passive safety system's countermeasures in a unit 330. Specifically, the computer device 110 can automatically activate the passive safety system 135, including multiple airbags, cushions, and / or seatbelt pretensioners. Depending on the probability of the driver's intent, the computer device 110 can deploy some or all of the passive safety system 135. The passive safety system's countermeasures include automatic pretensioning of the seatbelts, active deployment of the headrests, longitudinal adjustment of the seat rails, deployment of the seat cushions, and / or closing of the windows. The passive safety system's countermeasures can be suppressed if the perceived hazard would increase as a result. Next, the computer device 110 in a block 335 activates the headlight system's countermeasures. The computer device 110 can automatically activate the headlight system 140 depending on the probability of the driver's intent. The headlight system's countermeasures include automatic adjustment of the high beam, the headlight aim, and / or a modification of the headlights' photometric pattern. The headlight system's countermeasures can be suppressed if the hazard assessment would increase as a result. The computer devices 110 contain, in general, instructions that can be executed by one or more computer devices, such as those identified above, and for carrying out blocks or steps of the processes described above. The computer-executable instructions can be compiled or interpreted by computer programs created using various programming languages ​​and / or techniques, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, JavaScript, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, for example, from memory, a computer-readable medium, etc., and executes these instructions, thereby carrying out one or more processes, including one or more of the processes described herein.Such instructions and other data can be stored and transmitted using various computer-readable media. A file in the computer device 110 is generally a collection of data stored on a computer-readable medium, such as a storage medium, a read / write memory, etc. A computer-readable medium contains any medium that participates in providing data (e.g., instructions) that can be read by a computer. Such a medium can take many forms, including non-volatile media, volatile media, etc., but is not limited to these. Non-volatile media include, for example, optical or magnetic disks and other persistent storage. Volatile media include dynamic read / write memory (DRAM), which typically forms main memory. Common forms of computer-readable media include, for example,a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical medium, punched cards, punched tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or any other memory cartridge or any other medium that a computer can read from. With regard to the media, processes, systems, procedures, etc., described herein, it should be self-evident that, although the steps of such processes, etc., have been described as occurring in a specific, ordered sequence, such processes could be carried out with the described steps performed in a different order than that described herein. It should further be self-evident that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of the systems and / or processes are provided here for the purpose of illustrating certain embodiments and should in no way be interpreted as limiting the disclosed subject matter. The adverb "essentially", as used here, means that a form, a structure, a measurement, a quantity, a time, etc., may deviate from a precisely described geometry, a precisely described distance, a precisely described measurement, a precisely described quantity, a precisely described time, etc., due to imperfections in the materials, the processing, the manufacturing, etc. In the drawings, the same reference numerals denote the same elements. Furthermore, some or all of these elements could be modified. With regard to the components, processes, systems, methods, etc. described herein, it should be self-evident that these are provided for the purpose of illustrating certain embodiments and that they should in no way be interpreted to limit the claimed invention. Accordingly, it is understood that the above description is intended to be illustrative and not limiting. Many embodiments and applications other than the examples provided would be obvious to those skilled in the art upon reading the above description. The scope of protection of the invention should not be determined by reference to the above description, but instead by reference to the appended claims together with the full scope of protection to which such claims entitle the holder. It is anticipated and intended that future developments will take place in the techniques discussed herein and that the disclosed systems and methods will be incorporated into such future embodiments.Overall, it should be self-evident that the invention is capable of modification and variation and is only limited by the following claims. It is intended that all terms used in the claims are given their simple and normal meanings as understood by those skilled in the art, unless explicitly stated otherwise herein. In particular, the use of singular articles, such as "a," "the," "this," etc., should be read as representing one or more of the specified elements, unless a claim expressly limits this.

Claims

A system comprising: a computer having a processor and memory, the memory storing instructions executable by the computer to: identify a target object; identify a potential intersection of a host vehicle and the target object; identify one or more data collection devices to provide data for analyzing the intersection; collect data relating to the host vehicle and the target object from the data collection devices; develop a coordinate map of a surrounding environment at least partially based on the data collected from the host vehicle and the target object; identify a probable trajectory of the target object at least partially based on the coordinate map; generate a hazard assessment at least partially based on the probable trajectory;and to activate at least one of several vehicle safety systems, at least partially, based on a hazard assessment obtained by analyzing the map. System according to claim 1, wherein the data includes a velocity and an acceleration of the object, wherein the velocity includes a lateral velocity and / or a longitudinal velocity and the acceleration includes a lateral acceleration and / or a longitudinal acceleration, and wherein the hazard assessment is based on at least one braking hazard number, one steering hazard number and one acceleration hazard number, wherein the braking hazard number is a measure of a change in the longitudinal acceleration of the host vehicle to allow either the host vehicle to stop or the object to pass the host vehicle, the steering hazard number is a measure of a change in the lateral acceleration of the host vehicle to allow either the host vehicle or the object to clear an intersection zone, and the acceleration hazard number is a measure of a specific longitudinal acceleration to allow either the host vehicle or the object toto drive past the other host vehicle and the object. System according to claim 2, wherein the at least one vehicle safety system is selectively activated at least partially based on at least one of the lateral speed, the longitudinal speed, the lateral acceleration and the longitudinal acceleration. System according to claim 1, wherein the vehicle safety systems include at least one brake assist, warning, steering assist, torque assist, passive safety and headlight system. System according to claim 4, wherein the instructions include instructions to adjust the brake sensitivity of the brake assist system at least partially based on the hazard assessment. System according to claim 4, wherein the instructions further include instructions to adjust the steering wheel sensitivity of the steering assistance system at least partially based on the hazard assessment. System according to claim 4, wherein the instructions further include instructions to adjust an acceleration sensitivity of the torque support system at least partially based on the hazard assessment. System according to claim 4, wherein the instructions further include instructions to set at least one high beam activation and / or one target of the headlights and / or one photometric pattern of the headlights of the headlight system at least partially based on the hazard assessment. System according to claim 1, wherein the instructions further include instructions to integrate the map into a digital road network map which provides a road network map in Cartesian XYZ coordinates. System according to claim 1, wherein the target object is a target vehicle, wherein the instructions further include instructions to use the map to determine a probability of the intent of the driver of the target vehicle at least partially based on the identified probable trajectory of the target vehicle and a probability of the intent of the driver of the host vehicle, which is a measure of a probable trajectory of the host vehicle, and to generate a hazard estimate at least partially based on the probability of the intent of the driver of the target vehicle, which is a measure of a probability of a collision between the host vehicle and the target vehicle. A method comprising: identifying a target object; identifying a potential intersection between a host vehicle and the target object; identifying one or more data collection devices to provide data for analyzing the intersection; collecting data relating to the host vehicle and the target object from the data collection devices; developing a coordinate map of a surrounding environment at least partially based on the data collected from the host vehicle and the target object; identifying a probable trajectory of the target object at least partially based on the coordinate map; generating a hazard estimate at least partially based on the probable trajectory; and activating at least one of several vehicle safety systems at least partially based on the hazard estimate. The method of claim 11, wherein the data includes a velocity and an acceleration of the target object, wherein the velocity includes a lateral velocity and / or a longitudinal velocity and the acceleration includes a lateral acceleration and / or a longitudinal acceleration, and wherein the hazard assessment is based on at least one braking hazard number, a steering hazard number and an acceleration hazard number, wherein the braking hazard number is a measure of a change in the longitudinal acceleration of the host vehicle to allow either the host vehicle to stop or the target object to pass the host vehicle, the steering hazard number is a measure of a change in the lateral acceleration of the host vehicle to allow either the host vehicle or the target object to clear an intersection zone, and the acceleration hazard number is a measure of a specific longitudinal acceleration.to allow one of the host vehicle and target object to pass the other of the host vehicle and target object. Method according to claim 12, wherein the at least one vehicle safety system is selectively activated at least partially based on the lateral speed, the longitudinal speed, the lateral acceleration and the longitudinal acceleration. Method according to claim 11, wherein the vehicle safety systems include at least one brake assist, warning, steering assist, torque assist, passive safety and headlight system. The method according to claim 14, which further comprises adjusting the brake sensitivity of the brake assist system at least partially based on the hazard assessment. The method according to claim 14, which further comprises adjusting the steering wheel sensitivity of the power steering system at least partially based on the hazard assessment. The method of claim 14, which further comprises adjusting an acceleration sensitivity of the torque support system at least partially based on the hazard assessment. The method of claim 14, further comprising setting a high beam activation and / or a target of the headlights and / or a photometric pattern of the headlights of the headlight system at least partially based on the hazard assessment. The method of claim 11, further comprising integrating the map into a digital road network map which provides a road network map in Cartesian XYZ coordinates. The method of claim 11, wherein the target object is a target vehicle, the method further comprising using the map to determine a probability of the intent of the driver of the target vehicle at least partially based on the probable trajectory of the target vehicle and a probability of the intent of the driver of the host vehicle, which is a measure of a probable trajectory of the host vehicle, and to generate a hazard estimate at least partially based on the probability of the intent of the driver of the target vehicle, which is a measure of a probability of a collision between the host vehicle and the target vehicle.

Images