Patent Description:
Many newer vehicles have a rear cross traffic alert system for notifying a driver of the presence of a target vehicle within a specified alert zone. The rear cross traffic alert device detects objects of interest that may collide with the host vehicle. When the target vehicle has entered the alert zone, an audible, haptic or visual queue alerts the driver of the presence of the target vehicle.

Many conventional cross-path detection systems utilize a static alert zone defined in part by a static longitudinal threshold relative to the host vehicle. A problem with use of a single static threshold is that the required threshold should be different depending on the scenario (road, parking lot). In a parking lot, it would be preferable to extend the threshold to the width of a parking lane and alert the driver on all incoming targets. On a road, it would be preferred to reduce the threshold to include only targets travelling on a first lane. On busy roads, second lane alerts can be considered a nuisance and classified as a false positive by the customer.

<CIT>describes a collision avoidance system of a vehicle includes a sensor configured to be disposed at a vehicle for sensing exterior and forwardly of the vehicle. A processor is operable to process sensor data captured by the sensor to determine the presence of a pedestrian ahead of the vehicle and at or moving towards a path of travel of the vehicle. The processor determines a time to collision based on a determined distance to the pedestrian and determined speed of the pedestrian and speed of the vehicle. The collision avoidance system is operable to generate an alert to the driver of the vehicle at a threshold time before the determined collision with the pedestrian. Responsive to a parameter, the collision avoidance system adjusts the threshold time to generate the alert at an earlier time.

<CIT>describes a method and a device for driver assistance in which the warning threshold at which the driver is warned for example of a departure from the lane is adaptively adjusted as a function of the driver's state and/or the driving situation.

There is therefore a need for improved systems and methods for cross-path detection in order to balance different requirements in the definition of the alert zone. These and other needs are met by way of the present disclosure.

Systems and methods are presented herein for improved cross-path performance and responses to objects of interest positioned within a zone of interest adjacent a host vehicle.

According to a first aspect, a cross traffic alert system according to accompanying claim <NUM> is provided. Optional features are set out in the accompanying dependent claims. One or more processors integral to, or used by, the CTA system may be configured to receive the target object relative positional data detected by a target detection sensor, determine position and trajectory of different targets, and detect (or attempt to detect) an environmental state based on the received target data. A driving environment in the coverage zone may be identified by classification based on the detection (or not) of an established environmental state. For example, the driving environment may be classified based on filtered target object positional data when the environmental state is not established, or based on the quality of the environmental state information when the environmental state has been established from several targets. Once a driving environment has been determined, corresponding longitudinal alert thresholds are dynamically adjusted to a setting that corresponds to the determined driving environment.

In the disclosed embodiments, the driving environment may include a variety of scenarios including, for example, parking lots, roadways, blocked views, parking structures, types of roads, intersections, etc. If a target vehicle is detected within the longitudinal alert threshold corresponding to the classified driving environment, an alert may be generated. In parking lot driving environments, the longitudinal alert threshold could be extended to cover the width of an entire parking lane, or another dimension, either of which could comprise a user selection. In road driving environments, the longitudinal alert threshold could be reduced to cover only a specified lane (e.g., the driving lane) or portion of the road. If the driving environment is unknown or the decision is not mature enough, the threshold alert area settings may be defaulted to settings associated with a parking lot driving environment.

In some embodiments, the dynamic adjustment of the threshold alert area settings may occur gradually between a first threshold and a second threshold.

In some embodiments, the CTA system detects the environmental state by identifying tracks for all target objects in the detection zone, and filtering the identified tracks to remove target tracks of unlikely relevance. The tracks may also be filtered based on the respective ages of the target tracks. The tracks may also be filtered based on a possible target object trajectory change, where the possible target object trajectory change is estimated by solving a multiple hypothesis problem characterized by independent calculations across a first plurality of the time points, the multiple hypothesis problem supposing a plurality of possible cross-path angle solutions, each cross-path angle solution representing a corresponding possible trajectory for the target object. The target tracks may also be filtered based on a track stability measure, or upon a comparison of a detected speed environmental state of a target object to a minimum speed threshold.

In some embodiments, the CTA system may filter the target tracks based on a comparison of a shortest path distance of a target object to a maximum longitudinal distance threshold, where the shortest path distance is determined as a projection of a distance between a position of the host vehicle and a position of the target object along an axis perpendicular to a trajectory of the target object. A minimum target speed may be assumed to be typical in road driving environments, while a maximum target speed may be assumed in a parking lot environment.

The processor will determine the correct environmental state by collecting information from all objects of interest. From the targets, characteristics unique to a road environment will be identified, such as speed and position relative to the host vehicle. Once a road environment has been identified, this setting may be locked and the alert threshold set to the road setting regardless of the target speed.

In an embodiment where the driving environment is determined to be by default a parking lot, the adjustment of the longitudinal threshold setting may occur gradually from a more relaxed threshold that is typical of a parking lot scenario to a tighter threshold that is more typical of a road environment. The selection of the threshold is a function of the vehicle target speed and the aim is to provide a smooth transition between the two different thresholds required between the different environment.

In various embodiments, the object detection sensor may be mounted to rear, front or both ends of the host vehicle. The object detection sensor may comprise a radar transmitter and receiver, and/or an object tracking camera configured to capture images of the target objects, the object tracking camera being mountable in the host vehicle.

The foregoing and other objects, features and advantages will be apparent from the following, more particular description of the embodiments, as illustrated in the accompanying figures, wherein like reference characters generally refer to identical or structurally and/or functionally similar parts throughout the different views. The figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments, wherein:.

The present disclosure is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of embodiments of the present disclosure, in which like reference numerals represent similar parts throughout the several views of the drawings.

The following discussion of embodiments of cross-traffic detection and alerting (CTA) systems for determining whether a host vehicle is on a roadway or parking lot, and dynamically adjusting a threshold alert area in a zone of interest adjacent the host vehicle based on said determination is merely exemplary in nature, and is in no way intended to limit the disclosed embodiments or their applications or uses. Alternatives to the embodiments disclosed may be devised without departing from the scope of the disclosure. For example, the discussion below may particularly refer to a host vehicle engaging in backing out of a parking space. However, as will be appreciated by those skilled in the art, in alternate embodiments, the host vehicle may be engaged in a forward gear for driving out of the parking spot. The type of driving environment detected may include more than parking lots and roadways. For example, the techniques described herein may be extended to include determinations that the driving environment includes one or more blocked view, parking structure, particular types of roads, traffic intersections, etc. Similarly, many of the embodiments describe the use of automotive radar systems in acquiring dynamic positional information regarding target objects in a zone of interest adjacent the vehicle. However, alternative or additional types of sensing systems may be employed, such as cameras configured for object tracking.

Well-known elements of technologies associated with the embodiments will not be described in detail, or will be omitted, so as not to obscure the relevant details of the novel methods and apparatus. Likewise, the term "embodiment" and the descriptive language associated with each use of the term do not require that all embodiments include the discussed feature, limitation, advantage or mode of operation. It will be further understood that the terms "comprises", "comprising", "having", "includes" and/or "including", when used herein, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof.

Further, several embodiments are described in terms of sequences of actions to be performed by, for example, by a processor, or by "logic configured to" perform said actions.

With reference to the block diagram of <FIG>, described is an embodiment of a CTA system <NUM> and methods for improved cross-traffic detection and alerting implemented on a host vehicle <NUM> and utilizing a target object sensing system <NUM>, such as an automotive radar system, and a processor <NUM>. Generally speaking, the systems and methods of the present disclosure may utilize automotive radar sensing systems to classify whether a detection zone of interest adjacent, i.e., detection zone <NUM> in front of, or detection zone <NUM> behind host vehicle <NUM> (depending on which driving gear is engaged) comprises a parking lot, roadway, or other driving environment.

With reference to the high-level flow diagram <NUM> depicted in <FIG>, the CTA system <NUM> iteratively acquires (or receives from the sensing equipment) in step <NUM> target detection and tracking information associated with any target objects (targets) detected in either of the detection zones <NUM>, <NUM>. Automotive radar modules <NUM>-<NUM> on the host vehicle <NUM> track changes over time in positional information of one or more detected target(s) and processor <NUM> uses that information to determine environmental states associated with the detected targets. The driving environment can be classified by processor <NUM> as a roadway, parking lot, or some other driving scenario. Environmental state information from multiple tracks is typically needed to classify correctly the driving environment. Applicant notes, again, that while various example embodiments herein relate to the use of "radar" and "radar systems," the subject application is not limited to such radio wave based sensing. Rather, the systems and methods described herein may utilize any automotive proximity sensing detector that is able to provide range and angular position information for a target object, such as laser scanning (e.g., LIDAR) based systems, cameras or other image based sensing systems, and the like.

Processor <NUM> may classify (in step <NUM>) the driving environment based on the quality of (i.e., confidence in) environmental state information associated with the target objects, such as lane position and speed information, and optionally other classifying inputs. In step <NUM>, processor <NUM> detects environmental states, and if no, or low quality environmental state information has been established, the driving environment is classified as undecided and the alert threshold is dynamically adjusted according to target speed data (in step <NUM>). However, if processor <NUM> determines the quality of existing environmental state information to be adequately established, the driving environment will be classified (in step <NUM>) based on the environmental information provided by multiple targets. Once the driving environment in the detection zone (<NUM> or <NUM>) is classified as, for example, a roadway in step <NUM>, a proximity threshold alert setting may be permanently set. A target detected within a given alert threshold may prompt processor <NUM> to trigger an output signal to a response generator <NUM> that may activate an audio (e.g., via a speaker <NUM>) or visual (e.g., via a display device <NUM>, light, etc.) alarm, or an autonomous driving assist (e.g., braking, etc.) system <NUM> on the host vehicle <NUM>.

The CTA system <NUM> may be configured with two pairs of radar sensors, a right and left rear radar modules <NUM>,<NUM> and right and left front modules <NUM>, <NUM>, with a radar module mounted at each vehicle corner. The sensors <NUM>-<NUM> communicate with an electronic control unit (ECU) <NUM> that may communicate with and control CTA system <NUM>. The host vehicle <NUM> may also include one or more view mirrors that may be in communication with the ECU <NUM> and may include the visual or audio alert capability (speaker <NUM>, or a light, etc.) that can be activated by the response generator <NUM> of the ECU <NUM>. Alternatively, or in addition, the display device <NUM> may be mounted on an instrument panel and may be in communication with the ECU <NUM>. The ECU <NUM> may include memory, such as PROM, EPROM, EEPROM, Flash, or other types of memory, which may include data tables stored therein. The ECU <NUM> may include multiple separate processors in communication with one another and may be made up of various combinations of hardware and software as is known to those skilled in the art. The ECU <NUM> may also control one or more autonomous driving system that may be activated in response to a signal output from the CTA system <NUM>.

With reference to <FIG>, exemplary radar module <NUM> processes radar transmit and receive signals that are compatible with a radar system <NUM> mounted in the host vehicle <NUM>. Radar sensor module <NUM> generates and transmits radar signals into the detection zone <NUM> adjacent to the host vehicle that is being monitored by the radar system. Generation and transmission of signals is accomplished by RF signal generator <NUM>, radar transmit circuitry <NUM> and transmit antenna <NUM>. Radar transmit circuitry <NUM> generally includes any circuitry required to generate the signals transmitted via transmit antenna <NUM>, such as signal shaping/timing circuitry, transmit trigger circuitry, RF switch circuitry, RF power amplifier circuitry, or any other appropriate transmit circuitry used by radar system <NUM> to generate the transmitted radar signal according to exemplary embodiments described in detail herein. In some embodiments, the RF signal transmit circuitry <NUM> may include an RF switch mechanism may rely on inputs from an RF oscillator included in RF signal generator <NUM>. The RF signal transmit circuitry <NUM> may further advantageously include pulse shaping circuitry, e.g., based on transmit antenna trigonometric calculations.

Radar module <NUM> may also receive returning radar signals at radar receive circuitry <NUM> via receive antenna <NUM>. Radar receive circuitry <NUM> generally includes any circuitry required to process the signals received via receive antenna <NUM>, such as RF low noise amplifier circuitry, signal shaping/timing circuitry, receive trigger circuitry, RF switch circuitry, or any other appropriate receive circuitry used by radar system <NUM>. In some embodiments, radar receive circuitry <NUM> may also include a receiver antenna select module for selecting the receive antenna from a plurality of receive antennas. In some exemplary embodiments, the received signals processed by radar receive circuitry <NUM> are forwarded to phase shifter circuitry <NUM>, which generates two signals having a predetermined phase difference. These two signals, referred to as an inphase (I) signal and a quadrature (Q) signal, are mixed with an RF signal from RF signal generator <NUM> by mixers <NUM> and <NUM>, respectively, to generate I and Q intermediate frequency (IF) signals. In some embodiments mixing may further be based on pulse shaping of the RF signal from the RF signal generator <NUM> based on receive antenna trigonometric calculations. The resulting IF signals are further filtered as required by filtering circuitry <NUM> to generate filtered IF I and Q signals, labeled "I" and "Q" in <FIG>. The IF I and Q signals are digitized by analog-to-digital converter circuitry (ADC) <NUM>. These digitized I and Q IF signals are processed by a processor, such as a digital signal processor (DSP) <NUM>. In some exemplary embodiments, the DSP <NUM> can perform all of the processing required to carry out the object detection and parameter determination, including object range, bearing and/or velocity determinations, performed by CTA system <NUM>.

It will be understood that the system configuration illustrated in <FIG> is exemplary only and that other system configurations can be used to implement the embodiments described herein. For example, the ordering of filtering of the IF signal and analog-to-digital conversion may be different than the order illustrated in <FIG>. The IF signal may be digitized before filtering, and then digital filtering may be carried out on the digitized signal(s). In other embodiments, the entire IF stage may be removed so that the RF signal is directly converted to DC for further digitizing and processing.

In contrast with conventional cross-path detection systems which utilize a predetermined and static zone alert zone (e.g., as characterized by one or more overlapping regions of interest), the systems and methods of the subject application allow the alert zone to be dynamically adjusted as a function of the driving environment scenario determination. <FIG> show two example driving environments that the host vehicle <NUM> may encounter, i.e., backing into a roadway driving environment (<FIG>) and backing into a parking lot driving environment (<FIG>). A target vehicle <NUM> is shown traveling past the host vehicle <NUM> in both scenarios, within detection zone <NUM> adjacent host vehicle <NUM> at a distance that may or may not endanger the host vehicle <NUM>.

According to an embodiment, CTA system <NUM> is enabled when the host vehicle's forward or reverse gear is engaged and while the ignition is switched on. If the switch-on and reverse gear conditions are met, the CTA system <NUM> is initialized and data entries associated with previous operation may be cleared. The met conditions indicate to CTA system <NUM> that the driver intends to back up the host vehicle <NUM>. Upon system initialization, the radar system (or other sensing system) may be enabled to measure the range, angle and Doppler to target objects (e.g., target vehicle <NUM>) present in detection zone <NUM> or that are entering that area. If target vehicle <NUM> has not been noticed by the driver of host vehicle <NUM>, it is detected, and the CTA system evaluates, based on initial parking lot driving environment (i.e., an example default assumption if no contrary environment state information has been established) associated alert threshold settings and the target positional information, whether to activate an alert for the driver and/or an autonomous driving assist system (e.g., switching on one or more deceleration devices and applying the brakes of host vehicle <NUM> until it is stationary) in order to avoid a collision with target vehicle <NUM>.

The CTA system <NUM> may utilize predefined alert zones comprising regions with distinct alerting rules separated by a threshold comprising a longitudinal distance from host vehicle <NUM>. <FIG> illustrates an exemplary "must alert" zone <NUM> and a "must not" alert zone <NUM>, separated by alert threshold <NUM> and having settings associated with a roadway driving environment scenario. The "must not" alert zone <NUM> represents an environment or target that should not cause an alert, such as detected stationary objects, shopping carts, moving entry/exit doors, pedestrians, etc. In existing systems, the alert threshold <NUM> and must/must not zones <NUM>,<NUM> may be implemented, for example, upon determination that target vehicle <NUM> is traveling above a certain predetermined speed (e.g., above <NUM> mph). With respect to the example parking lot driving environment scenario shown in <FIG>, an exemplary "must alert" zone <NUM> and "may alert" zone <NUM> are separated by a distinct alert threshold <NUM>, all of which may be implemented based on the detected speed of target vehicle <NUM> exceeding predetermined speed in the detection zone <NUM>. Unfortunately, predefine alert zones can be both over and under inclusive with respect to an otherwise optimal zone of interest for a particular cross-traffic driving environment, resulting in inaccurate detection of a target object (such as providing a false indication of an impending collision or, worse yet, a delayed or inaccurate indication of an impending collision). For example, if target vehicle <NUM> is traveling at a high speed in a parking lot, the alert rules and settings described would result in a reduced alert threshold <NUM>, and thus a false negative (no alert when one should be issued) could occur. In contrast, road conditions (e.g., traffic light, targets turning on middle lane where vehicles turn into driveways, service drives, heavy traffic, bad weather, etc.) can product slower target vehicle speeds, which will result in wider "must alert' zones and produce an increase in false positive alerts. It is also known that target vehicles in a middle lane tend to travel slower than surrounding lanes. Use of state information associated with target vehicles determined to be in a middle lane may interfere with alert rules based solely on target vehicle speed.

Thus, CTA system <NUM> applies a method <NUM> (an example embodiment of which shown in flow diagrams of <FIG>) for detecting, learning and filtering tracked target object environmental state information, in order to classify the driving environment in either or both of the detection zone(s) <NUM>, <NUM> adjacent host vehicle <NUM>. Once the target environmental state information is established and meets selected conditions, the driving environment (e.g., a parking lot, roadway, or other scenario) may be classified, resulting in adaptive application of an appropriate corresponding alert threshold (and optionally, alert zones dimensions and rules) settings. Method <NUM> employs a target-speed based approach to setting alert zone(s) <NUM>, <NUM> and threshold <NUM>, in this embodiment corresponding to a parking lot driving environment, until the system learns through analysis of environmental state information that the driving environment represents a different scenario, such as a roadway. The transition between alert zone settings associated with the two driving environment scenarios may be abrupt, or gradual corresponding to target speeds between two transition zone speeds (such as transition range limits <NUM> and <NUM> as shown in <FIG>), which may avoid sudden changes when target speeds are hovering in borderline cases.

An embodiment of the main function of method <NUM> performed by CTA system <NUM> is depicted in <FIG>, which is invoked and iteratively/recursively executed after ECU <NUM> determines (step <NUM>) that the ignition of host vehicle <NUM> is 'on' and a forward or reverse gear of host vehicle is engaged (determination at step <NUM>). Method <NUM> calls upon several sub-functions, examples of which are depicted in <FIG> and which are described below. Whether the host vehicle is placed into reverse gear or forward gear will determine which radar modules should be activated to start gathering data in is associated detection zone.

At step <NUM>, inputs, such as radar information suitable to track one or more target object(s) detected in the detection zone is acquired. The radar data may be converted into target speed and x-y coordinates specifying the position of each detected target. Target tracks may then be determined for each detected target object using two or more data samples. As noted, various technologies and methods are known for determining object environmental state information (e.g., position, speed, trajectory, parking angle, etc.) for detected target objects. One such automotive radar technology is described in <CIT>, the contents of which are hereby incorporated by reference in their entirety. Target tracks can be characterized by the content and quality of the associated information. The information content is the proposition that, at a particular location in the detection zone, a target object may have particular environmental state information, and the quality is the strength with which that proposition is believed to be true. In some embodiments, the environmental state (i.e., parking lot, road, slow moving road, high speed road, etc.) is classified based on information content that can take many forms, including but not limited to target position, speed, driving lane, heading, acceleration and/or other information types such as GPS data, digital maps, real-time radio inputs and data from real-time transponders such as optical markers. The associated information quality can be evaluated under various reasoning frameworks, such as probability, fuzzy logic, evidential reasoning, or random set.

Multiple samples of target environmental state information for detected targets may be acquired over time. In some embodiments, the data sampling frequency of the sensing system may be actively adjusted. Iterative/recursive determination of the targets' environmental state information may enable adaptive windowing of target track information and the discarding of outlier data, resulting in higher quality state information. In preferred embodiments, cross-path detection may assume a fixed or static host vehicle <NUM>, e.g., such as a vehicle that is just preparing to back out of a parking space or driveway, or that is waiting at an intersection. Assuming a fixed or static host vehicle may advantageously simplify cross-path calculations. The cross-path target tracks may then be filtered in several ways to produce higher quality (more likely accurate) environmental state information.

It is noted that, for a given position of the host vehicle, shortest path distance is configured to remain constant regardless of a current orientation of the host vehicle (with only the relative orientation and not the magnitude of the shortest path distance vector changing). In this way shortest path distance may be used to represent apply a fixed buffer distance for the alert zone regardless of orientation of the host vehicle (for example by applying a fixed threshold with respect to shortest path distance). Thus, in some embodiments, a buffer width of the alert zone may remain constant with only an orientation of the alert zone changing relative to the host vehicle as determined based on the cross-path angle.

The CTA system <NUM> and methods described herein are configured to dynamically adjust a threshold alert area in the detection zone based on classifications of the driving environment therein. An output signal may then be generated, indicating when one or more of the target objects enter the threshold alert area. One or more processors may be configured to receive target object relative positional data detected by the object detection sensor, detect environmental states associated with the received target data, and perform classifications of the driving environment based on filtered target object positional data when no environmental state has been established, or based on the quality of environmental state information when the environmental state has been established.

While example embodiments and calculations described herein generally relate to a static host vehicle (in the interested of simplicity), it is noted that the present disclosure is not limited to such embodiments. Thus, in some embodiments, cross-path detection may further account for a moving host vehicle. In such embodiments, a position and orientation of the host vehicle at the second point in time may be known relative to the position and orientation of the host vehicle at the first point in time (e.g., based of GPS or other motion tracking of the host vehicle such as gyro or steering wheel angle, speedometer/odometer readings, etc.). This known relative position data may advantageously be used to offset the radar calculations at one or more of the points in time, e.g., so as to provide a common point of reference for the radar inputs (notably the common point of reference may be the host vehicle position and orientation at a first time point, the vehicle position and orientation at a second time point of some other common reference point for vehicle position and orientation). Thus, calculation of cross-path environmental state information may proceed based on the common point of reference using similar calculations described herein (essentially the use of the common reference point for host vehicle position and orientation reduces the calculation to one where the host vehicle is static).

At step <NUM> of method <NUM>, the system tries to find valid target tracks. An invalid target track has an expired track ID if it is considered to report too much noise to be useful. If no valid ID can be found, processing flows to the end of the main function. At step <NUM>, for target tracks having a valid ID, a determination is made whether a parking angle and target trajectory estimation of the (x,y) position of the target has been calculated, and satisfies defined convergency requirements and whether the target's speed is above a predetermined minimum speed, MIN_SPD <NUM>. If both conditions are true, the track is considered stable and further track filtering may occur. Otherwise, data associated with the track is cleared and processing flows to the end of the main function.

At step <NUM>, a determination is made whether the difference between the prior estimate of the longitudinal distance of the target to the host vehicle and the current estimate exceeds a maximum difference variance threshold, MAX_DIFF_VAR <NUM>. If the threshold is exceeded, data associated with the track is cleared and processing flows to the end of the main function. In step <NUM>, SpeedCriteria() function <NUM> is invoked.

<FIG> depicts a flow diagram for the SpeedCriteria() function <NUM> executed by processor <NUM>. The general purpose of function <NUM> is to generate, for each filtered track meeting the previously described conditions, a contribution toward the correct determination of the driving environment (e.g., parking lot, roadway, etc.) in the detection zone(s) <NUM>, <NUM> adjacent host vehicle <NUM>. Each track may contribute only once (per main function iteration) based on target speed environmental state information to either or both driving environment conditions. In the disclosed embodiments, multiple tracks contribute to the determination of the driving environment scenario, implemented as increments or decrements to a driving environment counter.

In step <NUM>, processor <NUM> determines whether the target speed exceeds a minimum threshold speed MIN_RD_SPD (which represents the minimum typical speed on a road) <NUM>. If the target speed is greater than the threshold speed MIN_RD_SPD <NUM>, the current track will contribute to the speed qualifier in step <NUM> after confirmation for the current track over several cycles. In step <NUM>, processor <NUM> determines whether the target speed is above an additional threshold speed HIGH_RD_SPD <NUM> (which represents a typical two lane road speed that will never be achieved in a parking lot). If the target speed is greater than HIGH_RD_SPD <NUM>, then in step <NUM> the current track will have an increased contribution towards the road environment classification decision.

With reference again to main function of method <NUM> depicted in <FIG>, in step <NUM> processor <NUM> invokes two sub-functions: a second driving environment classification function, LaneCriteria() function <NUM> (depicted in <FIG>); and a dynamic alert threshold setting function, SetAlertThreshold() function <NUM> (depicted in <FIG>) for initializing and dynamically adjusting the alert settings (zones <NUM>-<NUM> and/or alert thresholds <NUM>, <NUM>) in the detection areas <NUM>, <NUM> adjacent the host vehicle <NUM>, based on the classified driving environment. In preferred embodiments, the alert threshold <NUM>, <NUM> is defaulted to a threshold corresponding to a parking lot driving environment scenario, until sufficient data acquisition and classification indicates with confidence that the driving environment is actually another environment, such as a roadway.

Lane criteria function <NUM> (depicted in <FIG>) generates additional contributions toward the determination of the parking lot or roadway driving environment, by further classifying valid environmental state (e.g., filtered target track, etc.) information entries. Similarly, each track can only contribute based on its associated target lane information once toward either driving environment conditions. For every available valid track, it is compared against one or more longitudinal distance additional targets to determine if two independent targets are travelling in two different lanes (step <NUM>). A determination is also made (step <NUM>) whether the calculated distance is beyond a third lane, which will indicate a target travelling on the road. If either condition is true (step <NUM> or step <NUM>) a lane qualifier counter is used to confirm the lane criteria over several cycles (step <NUM>) and if the lane qualifier criteria is confirmed across several cycles for the same target, a lane quality contribution from the track is added to the road driving environment.

The SetAlertThreshold() function <NUM> called upon by processor <NUM> in step <NUM> of the main function <NUM> (and depicted in <FIG>) operates to determine (step <NUM>) whether the driving environment counter exceeds a predefined threshold value indicating the presence of a roadway in the detection zone. If a roadway condition is not detected, then in step <NUM>, the driving environment is classified as undefined, and the alert threshold (distance) settings are based on the target speed (as in example <FIG>). From a target speed of <NUM> mph to <NUM> mph the threshold setting may be associated with the parking lot driving environment scenario. If, however, the driving environment counter exceeds the roadway threshold value, then in step <NUM> the driving environment is classified as a roadway, and the alert threshold (distance) settings associated with the roadway scenario are implemented for the detection zone. As described above, the transition of alert threshold settings may occur abruptly or gradually.

Whereas many alterations and modifications of the disclosure will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that the particular embodiments shown and described by way of illustration are in no way intended to be considered limiting. No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Where only one item is intended, the term "one" or similar language is used. Further, the phrase "based on" is intended to mean "based, at least in part, on" unless explicitly stated otherwise.

Claim 1:
A cross traffic alert system for a host vehicle engaged in a forward or reverse gear position, comprising:
a sensing system mountable to said host vehicle and configured to detect relative positions of a plurality of target objects present in a zone of interest proximate to the host vehicle in a direction corresponding to the engaged gear position and over multiple time points; and
a processor configured to:
receive target object relative positional data detected by the sensing system, and
generate an output signal indicating when one or more of the target objects has entered a threshold alert area; characterized in that the processor is configured to:
dynamically adjust the threshold alert area based on the received target object relative positional data wherein the threshold alert area is adjusted to have a first longitudinal alert threshold when the received target object relative positional data compares to a first classification of driving environment, and the threshold alert area is adjusted to have a second longitudinal alert threshold when the received target object relative positional data compares to a second classification of driving environment.