Traffic flow risk prediction and mitigation

Abstract

A method for: determining a risk boundary in response to multiple indications of an emergency braking event, wherein the risk boundary indicates multiple speed-flow pairs at which a risk of an emergency braking event is below a threshold; determining a set of speed-flow pairs of average speed and vehicle count and the multiple indications of the emergency braking event at a road segment level; determining a host vehicle speed; and performing at least one of reducing the host vehicle speed and increasing the host vehicle following distance in response to the host vehicle speed exceeding the risk boundary for vehicle flow density.

Description

Technical Field

[0001] The present disclosure generally relates to systems for predicting traffic flow disruption risk and providing traffic flow information to motor vehicles and infrastructure. More specifically, aspects of the present disclosure relate to systems, methods, and apparatus for determining the traffic flow risk of a speed-flow pair in a road segment in response to the number of hard braking events associated with the speed-flow pair. Background Art

[0002] An emergency braking event is an undesirable vehicle control event that indicates that the braking force of the vehicle has exceeded a threshold. An example of an emergency braking event would be when the vehicle speed decreases at a rate greater than seven miles per hour per second. The number of close braking events at a location can indicate an increased risk of vehicle-to-vehicle contact at that location or a disruption in traffic flow at that location. These emergency braking events can be caused by driver inattention, debris on the road, hazardous or slippery road conditions, changes in lighting conditions, changes in vehicle flow patterns, stop-start traffic, construction, or existing accidents. It has been shown that as the density of vehicles on a road increases, the number of emergency braking events may also increase. Similarly, as the average speed of vehicles on a road increases, the number of emergency braking events may also increase.

[0003] Estimating the likelihood of high-risk road or traffic conditions can be difficult for drivers or fleet operators because the causes of these high-risk events are highly dependent on constantly changing traffic flow information. Traditional time-of-day estimates estimate traffic flow risk based on the average traffic flow risk at a specific time of day over many past days, without considering current conditions on the road segment. Furthermore, some drivers may be more tolerant of risk than others, driven by travel time reduction or route preferences. It would be desirable to provide absolute traffic flow risk assessments for on-road vehicle operators and infrastructure managers while overcoming the aforementioned issues.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0005] Disclosed herein are vehicle sensor methods and systems for providing a vehicle system, along with associated control logic, methods for manufacturing and operating such systems, and motor vehicles equipped with such systems. By way of example and not limitation, various embodiments of systems are presented for determining a real-time risk assessment of vehicle flow disruptions in response to maximizing the number of speed-flow pairs and the number of emergency braking events associated with each speed-flow pair. This risk assessment can be provided to the driver, the vehicle navigation system, and infrastructure management systems.

[0006] According to one aspect of the present disclosure, an apparatus includes: a user interface configured to receive a driver risk tolerance in response to a user input; a receiver configured to receive a risk assessment for a road segment, wherein the risk assessment is generated in response to a vehicle flow rate on the road segment, an average vehicle speed on the road segment, and a number of emergency braking events occurring within the road segment; and a vehicle controller configured to reduce a vehicle speed to a reduced vehicle speed in response to the risk assessment exceeding the driver risk tolerance.

[0007] According to an aspect of the present disclosure, wherein the vehicle speed is reduced in response to an updated risk assessment matching a driver risk tolerance, and wherein the updated risk assessment is determined in response to a vehicle flow rate on the road segment, a number of emergency braking events occurring within the road segment, and the reduced vehicle speed.

[0008] According to one aspect of the present disclosure, a memory is provided for storing a plurality of risk assessments corresponding to a plurality of vehicle flow rate and average vehicle speed pairs.

[0009] According to an aspect of the present disclosure, the driver risk tolerance is determined in response to previous driver behavior.

[0010] According to one aspect of the present disclosure, a display is provided for providing an indication of an increased following distance to a driver.

[0011] According to an aspect of the present disclosure, the vehicle controller is further operable to increase the following distance in response to the risk assessment exceeding the driver's risk tolerance.

[0012] According to an aspect of the present disclosure, wherein the risk assessment is proportional to the number of emergency braking events that occurred within the road segment during a previous time interval.

[0013] According to an aspect of the present disclosure, further included is a navigation system for determining an alternate route in response to the risk assessment exceeding the driver's risk tolerance.

[0014] According to an aspect of the present disclosure, a vehicle controller is configured to perform a lane change maneuver in response to a risk assessment exceeding a driver risk tolerance.

[0015] According to one aspect of the present disclosure, a method includes: receiving multiple indications of an emergency braking event, wherein each of the multiple indications includes a location and a vehicle speed; determining a risk boundary in response to the multiple indications of the emergency braking event, wherein the risk boundary indicates a plurality of speed-flow pairs at which a risk of the emergency braking event is below a threshold; determining a vehicle flow density for a road segment; determining a host vehicle speed; and performing at least one of reducing the host vehicle speed and increasing the host vehicle following distance in response to the host vehicle speed exceeding the risk boundary for the vehicle flow density.

[0016] According to an aspect of the present disclosure, further comprising selecting an alternative navigation route in response to a risk boundary for vehicle traffic density.

[0017] According to an aspect of the present disclosure, the risk boundary is further determined in response to user input.

[0018] According to one aspect of the present disclosure, the risk boundary is determined using a Greenshield traffic flow model.

[0019] According to an aspect of the present disclosure, the risk boundary is further determined in response to a third-party risk management selection.

[0020] According to another aspect of the present disclosure, an alternate navigation route is generated in response to a host vehicle speed exceeding a risk boundary for vehicle traffic density.

[0021] According to another aspect of the present disclosure, a driving lane is recommended in response to a host vehicle speed exceeding a risk boundary for vehicle flow density.

[0022] According to another aspect of the present disclosure, a host vehicle lane change maneuver is performed in response to a host vehicle speed exceeding a risk boundary for vehicle traffic density.

[0023] According to another aspect of the present disclosure, a driver alert is provided to a host vehicle driver in response to a host vehicle speed exceeding a risk boundary for vehicle traffic density.

[0024] According to another aspect of the present disclosure, the risk boundary is determined for a speed-flow pair in response to a probability of emergency braking occurring within the road segment.

[0025] According to another aspect of the present disclosure, an advanced driver assistance system includes: receiving a driver risk tolerance via a user interface; determining, by a processor, a risk assessment for a road segment, wherein the risk assessment is generated in response to a vehicle flow rate on the road segment, an average vehicle speed on the road segment, and a number of emergency braking events occurring within the road segment; and reducing a vehicle speed in response to the risk assessment exceeding the driver risk tolerance.

[0026] According to another aspect of the present disclosure, the vehicle following distance is increased in response to the risk assessment exceeding the driver's risk tolerance.

[0027] The above advantages and other advantages and features of the present disclosure will be apparent from the following detailed description of preferred embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Exemplary embodiments will be described hereinafter with reference to the following drawings, wherein like numerals represent like elements, and wherein:

[0029] Figure 1 An exemplary environment for using traffic flow risk prediction according to an exemplary embodiment of the present disclosure is shown;

[0030] Figure 2 An exemplary graphical representation showing a plurality of indications of an absolute minimum boundary and an emergency braking event set according to an exemplary embodiment of the present disclosure;

[0031] Figure 3 A block diagram illustrating a system for traffic flow risk prediction in a motor vehicle according to an exemplary embodiment of the present disclosure is shown;

[0032] Figure 4 A flowchart illustrating an exemplary method for traffic flow risk prediction according to an exemplary embodiment of the present disclosure is shown; and

[0033] Figure 5 Another block diagram illustrating a system for traffic flow risk prediction in infrastructure according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0034] The following detailed description is merely exemplary in nature and is not intended to limit application and use. Furthermore, the present invention is not intended to be bound by any theory, expressed or implied, presented in the foregoing technical field, background technology, brief summary, or the following detailed description. As used herein, the term module refers to an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0035] Emergency braking events are often used as a surrogate measure of road performance and / or driver performance. For example, a commercial driver with multiple emergency braking events may be considered to be at a higher risk than a commercial driver without emergency braking events. Higher-risk drivers can then be identified as candidates for additional training to reduce risky driving behavior. Emergency braking events can also indicate disruptive traffic flow or hazardous traffic conditions, such as construction zones, the location of immobile vehicles, or the location of emergency service personnel. The driver or the controller of a vehicle equipped with an advanced driver assistance system (ADAS) may be required to adjust the navigation route or vehicle parameters, such as speed or following distance, in order to reduce potential risk.

[0036] In an exemplary embodiment, data indicative of emergency braking events is collected by an infrastructure management system, a vehicle control system, crowdsourcing, or the like. A risk threshold boundary is then generated in response to an acceptable risk level for the driver or infrastructure manager. The threshold boundary is the maximum vehicle speed for a particular traffic flow density at which the risk is at an acceptable risk level. If the vehicle is expected to exceed the threshold boundary at an upcoming location, the driver or ADAS may decide to reduce the vehicle speed, increase the following distance from the preceding vehicle, and / or select an alternate route. In the case of an exemplary traffic flow management system, the speed limit approaching the upcoming location may be lowered and / or warning signs activated to reduce the risk to an acceptable level. Additionally, access may be restricted on controlled access roads to reduce vehicle flow on the road segment.

[0037] Now turn Figure 1 , illustrates an exemplary environment 100 of a vehicle with a traffic flow risk prediction system according to an exemplary embodiment of the present disclosure. Exemplary environment 100 depicts a section of multi-lane road 115, a roadside communication unit 165, a first vehicle 110, a second vehicle 107, a third vehicle 130, and a host vehicle 150. In the exemplary embodiment, the distance between second vehicle 107 and host vehicle 150 is a following distance 155.

[0038] The exemplary system is first configured to detect multiple emergency braking events within the segment of multi-lane roadway 115. In some exemplary embodiments, the length of the segment is five hundred meters. The emergency braking events may be detected by one or more of the plurality of vehicles 110, 107, 130, 150 and transmitted to a roadside communication unit 165 or other wireless network, to a processing center, etc. The initial speed at the start of each emergency braking event and the vehicle density for each emergency braking event may also be detected and stored along with the emergency braking event. The speed and position of each vehicle in the plurality of vehicles 110, 107, 130, 150 may also be transmitted from each vehicle in the plurality of vehicles 110, 107, 130, 150 to the roadside communication unit 165. Alternatively, the emergency braking events and the speeds and positions of the plurality of vehicles may be detected via alternative equipment, such as roadside traffic flow detection equipment 167, or may be transmitted from a mobile device within one or more of the plurality of vehicles 110, 107, 130, 150. The roadside traffic flow detection device 167 may use road pressure sensors, imaging technology, radar, lidar, infrared detection or other remote sensing technology to detect the position and speed of the plurality of vehicles 110 , 107 , 130 , 150 .

[0039] The exemplary system may then be operable to generate a speed-flow boundary that excludes the set in the emergency braking set while maximizing the number of speed-flow pairs having a number of emergency braking events below a threshold number. In some exemplary embodiments, the boundary may be generated using a Greenshield traffic flow model and adjusting flow and / or density such that the boundary is below the speed-flow pairs having a number of emergency braking events above a threshold number. Figure 2 A graphical representation 200 shows an absolute minimum boundary 220 and multiple indications 210 of a set of emergency braking events. A speed limit 215 for the road is also indicated. The graphical representation also indicates additional boundaries 205, 225 with boundaries above the absolute minimum boundary. These additional boundaries may indicate a higher risk of disruptiveness. For example, the absolute minimum boundary 220 may indicate a free traffic flow or a minimal risk driving environment. The second boundary 205 may indicate a synchronized free flow and a low risk to traffic flow, and the third boundary 225 may indicate a higher traffic flow with an increased risk of disruption.

[0040] The exemplary system combines established traffic flow theory with connected vehicle telemetry to assess road performance at each road segment. This collected information can be used to improve road safety performance across the road network in real time based on connected vehicle data. Traffic engineers and / or infrastructure managers can use dynamic message signs to notify road users or inform vehicle drivers of current road conditions.

[0041] In some exemplary embodiments, vehicle flow can be measured by the count of unique vehicles in the traffic flow on a road segment during a specific time period. This value can be assumed to be proportional to the actual vehicle flow. Using the Greenshield traffic flow model in a novel way, an optimized speed flow boundary can be generated to minimize the set of emergency braking events (10 or more) within the boundary. The boundary is used to determine how many vehicles should be in the vehicle flow at any given speed in order to minimize the risk of interruption. The extent to which the vehicle count exceeds the minimization value determines the risk caused by the traffic volume at the given speed. The density of emergency braking events is used to identify the minimized absolute risk boundary. Once identified, the boundary can be used to assess the vehicle spacing risk based on the average speed and vehicle count observed in samples from the spatiotemporal framework.

[0042] The exemplary systems and methods can be used to control the relationship between vehicles and traffic flow on a road segment and to determine attributes of a road segment based on networked vehicle data, established road profile data, and deviations from the road profile for the road segment. Furthermore, the systems can be used as input for vehicle navigation based on (i) selecting an alternative route for the vehicle, (ii) recommending a target lane or performing a lane change for the vehicle, (iii) changing the headway dynamics for the vehicle, (iv) changing the speed of the vehicle, and (v) changing the speed profile of the vehicle.

[0043] Now turn Figure 3 , a block diagram illustrating a system 300 for implementing a traffic flow risk prediction method according to an exemplary embodiment of the present disclosure is shown. The exemplary system 300 may include an antenna 305, a receiver 310, a processor 320, a driver information center 330, a vehicle controller 332, a camera 355, and an object detection system 345.

[0044] Antenna 305 is configured to receive electromagnetic signals including data via a wireless network communication channel, such as a broadband cellular network, vehicle-to-infrastructure (V2I) communication, vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or other wireless data transmission network. The data may carry data related to emergency braking events within a road segment. In some exemplary embodiments, the data may be a lookup table, etc., indicating the number of emergency braking events associated with a speed-flow pair. Alternatively, the exemplary lookup table may include traffic flow risks for multiple speed-flow pairs. The traffic flow risk for each speed-flow pair may be determined in response to the number of emergency braking events within the road segment at a specific average speed and traffic flow. In some exemplary embodiments, different traffic flow risks may be provided for each of the multiple speed-flow pairs for different times, dates, and / or seasons. Antenna 305 is communicatively coupled to receiver 310.

[0045] The receiver 310 may be configured to receive an electromagnetic signal from the antenna 305, demodulate the electromagnetic signal to extract a baseband signal, and decode the baseband signal to extract data. For example, 5G cellular signals have carrier modulation frequencies of approximately 28 GHz and 39 GHz. 5G data may also be encoded using orthogonal frequency division multiplexing (OFDM). The extracted data may then be coupled to the processor 320.

[0046] Processor 320 is operable to receive demodulated and decoded data from receiver 310. Processor 320 then uses the received data to determine the traffic flow risk for the upcoming road segment. The processor is also operable to receive the host vehicle's speed from vehicle sensors (such as vehicle controller 332 or GPS) and the host vehicle following distance of the host vehicle from object detection system 345. Object detection system 345 can be a lidar, radar, ultrasonic, light-emitting diode (LED), or other sensor configured to determine the distance between the host vehicle and a preceding vehicle within a road lane. Alternatively, the following distance can be estimated in response to images captured by host vehicle camera 355. The following distance can also be determined in response to V2I communications, where the infrastructure is operable to transmit vehicle position, speed, and the like, including the speed and position of the host vehicle and preceding vehicles.

[0047] Processor 320 is then configured to determine a traffic flow risk for the upcoming road segment in response to the host vehicle speed and the host vehicle following distance. The host vehicle speed and host vehicle distance can be used to select an applicable speed-flow pair and the associated traffic flow risk. Processor 320 then compares the traffic flow risk to a risk threshold. The risk threshold can be determined in response to user input, user behavior, and / or a defined threshold. The defined threshold can be established by a fleet management entity, infrastructure management, or other third party. User input can be received via driver information center 330 or other user input. User behavior can be estimated based on previous driver reactions to similar traffic flow risk situations. For example, data indicating an increase in following distance and / or a decrease in speed in response to previous traffic flow risk situations can be received from the vehicle controller.

[0048] If the traffic flow risk exceeds the risk threshold, the processor 320 is then configured to generate a control signal for transmission to the vehicle controller 332 to reduce the traffic flow risk of the host vehicle. The control signal may indicate a request for a lower host vehicle speed or an increased following distance. Alternatively, the processor 320 may be configured to determine an alternate route for the host vehicle to avoid the upcoming road segment. Alternatively, the processor 320 may be configured to display a recommended host vehicle speed or increased following distance to the driver via the driver information center 330 or other user input.

[0049] Now turn Figure 4 , a flow chart illustrating an exemplary method 400 for implementing traffic flow risk prediction within a road segment according to an exemplary embodiment of the present disclosure is shown. The exemplary method is first operable to receive 410 a plurality of indications of emergency braking events within a road segment. Each of the plurality of indications of the emergency braking event may include an initial speed of the vehicle prior to the emergency braking event, a position of the vehicle during the emergency braking event, and a time of the emergency braking event. Each of the plurality of indications of the emergency braking event may also include a traffic flow density at the time of the emergency braking event. If the traffic flow density is not included in the indication, the method may then be operable to determine the traffic flow density of the road segment at the time of the emergency braking event, or otherwise retrieve the traffic flow density at the time of the emergency braking event. The traffic flow density may be determined in response to adjacent infrastructure detection equipment, crowdsourced data, or in response to other available methods.

[0050] In some exemplary embodiments, instead of receiving individual indications of emergency braking events, a host vehicle may receive a traffic flow risk table via a wireless communication system, etc. The traffic flow risk table may provide traffic flow risk for each of a plurality of speed-flow pairs. The traffic flow risk table may be generated by an infrastructure server, etc., which receives information related to a plurality of emergency braking events and generates a table. The traffic flow risk table may then be transmitted to various vehicles and other infrastructure management devices.

[0051] The method may then be operable to associate a traffic flow risk with each of a plurality of speed-flow pairs 420. A speed-flow pair is a flow rate of vehicles per minute for a particular average vehicle speed within a road segment. In response to a plurality of emergency braking events for each speed-flow pair, the method generates a traffic flow risk. In some exemplary embodiments, the number of emergency braking events associated with a speed-flow pair is proportional to the traffic flow risk. For example, there may be more emergency braking events associated with a high-speed, high-vehicle density scenario than with a low-density, high-speed scenario or a high-density, low-speed scenario. Additionally, the traffic flow risk may be determined in response to a linear regression of observed emergency braking events between adjacent speed-flow pairs.

[0052] The method then determines 430 the host vehicle speed and following distance. The host vehicle speed and following distance may be determined in response to data from the host vehicle controller and host vehicle sensors, such as a speedometer, lidar, and / or global positioning system (GPS). The following distance is inversely proportional to the traffic flow rate. The greater the average following distance between vehicles, the fewer vehicles are within a given road segment at any given time.

[0053] Next, a traffic flow risk associated with the host vehicle's speed and following distance is determined 435 and compared 440 to a risk threshold. The risk threshold can be determined in response to user input, user behavior, and / or a defined threshold. The defined threshold can be established by a fleet management entity, infrastructure management, or other third party. If the traffic flow risk does not exceed the risk threshold, the host vehicle continues at the current host vehicle speed and following distance.

[0054] If the traffic flow risk associated with the current vehicle speed and following distance exceeds a threshold, the method is next configured to mitigate the risk 450 to an acceptable level. To mitigate the risk, the driver or ADAS may reduce at least one of the host vehicle speed and following distance. Generally, reducing the host vehicle speed or increasing the following distance reduces the apparent traffic flow rate and thus reduces the traffic flow risk. The method may reduce the vehicle speed and / or increase the following distance until the current traffic flow risk is less than or equal to the threshold. Alternatively, the method may select an alternate route to avoid the traffic flow risk at the upcoming road segment.

[0055] Now turn Figure 5 , a block diagram of a system 500 for traffic flow risk infrastructure management according to an exemplary embodiment of the present disclosure is shown. Exemplary system 500 may include a receiver 520, a transmitter 570, a display 540, a processor 530, an object detection system 550, a camera 510, and a memory 560. Exemplary system 500 may form part of an infrastructure management system for controlling traffic flow on a road segment.

[0056] System 500 can first be configured to receive multiple indications of emergency braking events within a road segment. These indications can be received from vehicles traveling on the road segment via receiver 520. These indications can be transmitted via a V2X communication network, for example. These indications can indicate the time and date of the emergency braking event, the initial vehicle speed, and the location of the event. Processor 530 can then be configured to determine a traffic flow risk within the road segment in response to the multiple indications of emergency braking events. Traffic flow risks can be calculated for multiple speed-flow pairs. These traffic flow risks can be stored in memory 560 communicatively coupled to processor 530.

[0057] The processor 530 is then configured to determine the current vehicle flow and average vehicle speed on the road segment. The current vehicle flow and average vehicle speed may be determined in response to crowdsourced data from individual vehicles or vehicle occupants, from data captured via the object detection system 550, and / or from image processing algorithms executed on images captured by the camera 510.

[0058] Processor 530 is then configured to compare the current vehicle flow and average vehicle speed on the road segment with the traffic flow risk for that speed-flow pair. If the traffic flow risk exceeds a threshold risk value, processor 530 may then determine an appropriate reduction in average vehicle speed and / or vehicle flow that will meet or fall below the threshold risk value. In some exemplary embodiments, processor 530 may then couple this reduced vehicle speed to a display 540, such as a variable speed limit sign, before and / or along the road segment. The vehicle following distance associated with the vehicle flow with the reduced risk value may be displayed on a programmable message board or traffic sign to provide the driver with a suggested increased following distance. Alternatively, the reduced vehicle speed and / or following distance may be provided directly to vehicles approaching the road segment via V2I communication for use by the driver and / or ADAS system.

[0059] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it will be understood that there are a large number of variations. It will also be understood that the exemplary embodiment or exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the present disclosure in any way. On the contrary, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing the exemplary embodiment or exemplary embodiments. It will be understood that various changes may be made in the function and arrangement of elements without departing from the scope of the present disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A device for traffic flow risk prediction, comprising: a user interface configured to receive a driver risk tolerance in response to user input; a receiver configured to receive data indicative of a plurality of emergency braking events, wherein each of the plurality of emergency braking events is associated with one of a plurality of speed-flow pairs; a processor for determining a risk assessment for the road segment in response to a host vehicle speed, a host vehicle following distance, and the number of emergency braking events, wherein the risk assessment is determined in response to a speed-flow boundary, and wherein the speed-flow boundary excludes a first portion of the plurality of speed-flow pairs having a number of emergency braking events above a threshold number and includes a second portion of the plurality of speed-flow pairs having a number of emergency braking events below a threshold number; as well as A user interface is provided for displaying at least one of a reduced vehicle speed and an increased following distance in response to the risk assessment exceeding the driver risk tolerance.

2. The apparatus of claim 1 , wherein the vehicle speed is reduced in response to an updated risk assessment matching the driver risk tolerance, and wherein the updated risk assessment is determined in response to a vehicle flow rate on the road segment, the plurality of emergency braking events occurring within the road segment, and the reduced vehicle speed. 3 . The apparatus of claim 1 , further comprising a memory for storing a plurality of risk assessments corresponding to a plurality of vehicle flow rate and average vehicle speed pairs. The apparatus of claim 1 , wherein the driver risk tolerance is determined in response to previous driver behavior.

5. The apparatus of claim 1 further comprising a display for providing an indication of the increased following distance to the driver.

6. The apparatus of claim 1 , further comprising a vehicle controller configured to reduce a vehicle speed to a reduced vehicle speed in response to the risk assessment exceeding the driver risk tolerance, wherein the vehicle controller is further operable to increase a following distance in response to the risk assessment exceeding the driver risk tolerance.

7. The apparatus of claim 1, wherein the risk assessment is a function of average speed and vehicle flow relative to a number of emergency braking events that occurred within the road segment during a previous time interval. 8 . The apparatus of claim 1 , further comprising a navigation system for determining an alternate route in response to the risk assessment exceeding the driver risk tolerance. 9 . The apparatus of claim 1 , wherein a vehicle controller is configured to perform a lane change maneuver in response to the risk assessment exceeding the driver risk tolerance.

10. A method for traffic flow risk prediction, comprising: receiving data indicative of a plurality of emergency braking events, wherein each of the plurality of emergency braking events is associated with a speed-flow pair; determining a risk boundary in response to the plurality of emergency braking events, wherein the risk boundary indicates a plurality of speed-flow pairs at which a risk of an emergency braking event is below a threshold; Determine vehicle flow density on a road segment; determining a host vehicle speed; as well as At least one of reducing the host vehicle speed and increasing a host vehicle following distance is performed in response to the host vehicle speed exceeding the risk boundary for the vehicle traffic density.

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