RULE-BASED ADAPTIVE SPEED CONTROL SYSTEM FOR A VEHICLE

Abstract

A rule-based adaptive cruise control system for determining a vehicle's following behavior comprises multiple perception sensors for collecting perception data representing the environment surrounding the vehicle, and one or more controllers in electronic communication with these sensors. The controller(s) categorize a road profile, based on a route plan and the perception data, into one of several road profiles, each specifying the road geometry. The system then categorizes the detection of a vehicle ahead, based on the perception data, into one of several detection states. Finally, it classifies the selected road profile and detection state into one of several scenarios based on a set of classification rules and assigns a specific following behavior based on the selected scenario.

Description

INTRODUCTION

[0001] The present disclosure relates to a rule-based adaptive cruise control system for a vehicle that determines vehicle following behavior based on a set of classification rules that take into account the profile of the roadway and a detection state of a preceding vehicle.

[0002] Many vehicles include various driver assistance systems that support the driver in a variety of ways. Adaptive cruise control (ACC) systems, for example, can relieve drivers of routine longitudinal vehicle control by ensuring that the host vehicle is within an acceptable forward travel distance of a vehicle immediately ahead of the host vehicle, referred to as the leading vehicle. Adaptive cruise control systems determine the longitudinal control of a host vehicle based on the movement of the leading vehicle.

[0003] It can be seen that current adaptive cruise control systems do not consider route planning information and instead determine the behavior of the host vehicle based on perception data regarding the vehicle ahead and a target speed. The perception data regarding the vehicle ahead is collected by perception sensors such as a camera. Consequently, if the vehicle ahead is currently leaving the field of view or is undetectable by the host vehicle's perception sensors due to the road geometry, the adaptive cruise control system assumes that the vehicle ahead is no longer present and adjusts the host vehicle's speed accordingly.For example, if the vehicle ahead negotiates a sharp bank angle in the roadway, a steep hill, a roundabout, or a merging lane, the vehicle ahead may temporarily leave the field of view of the host vehicle's perception sensors, and the adaptive cruise control system may then instruct the host vehicle to increase its speed to meet the target speed. However, once the vehicle ahead returns to the field of view of the perception sensors, the adaptive cruise control system may then instruct the host vehicle to decrease or change its speed. Therefore, adaptive cruise control systems may unnecessarily increase and decrease a vehicle's speed, which increases energy consumption and can adversely impact customer satisfaction.

[0004] Consequently, although adaptive cruise control systems serve their intended purpose, there is a need in the art for an improved method to regulate the speed of a host vehicle when the leading vehicle is not currently detected by the host vehicle's perception sensors. SUMMARY

[0005] According to several aspects, a rule-based adaptive cruise control system for determining vehicle-following behavior of a vehicle is disclosed. The rule-based adaptive cruise control system includes a plurality of perception sensors for collecting perception data representing an environment surrounding the vehicle, wherein the environment includes a roadway along which the vehicle is traveling, and the perception sensor includes a field of view. The rule-based adaptive cruise control system also includes one or more controllers in electronic communication with the plurality of perception sensors. The one or more controllers execute instructions to receive a route plan and the perception data collected by the plurality of perception sensors.The one or more controllers categorize a profile of the roadway based on the route plan and the perception data into one of a plurality of roadway profiles, each indicating a geometry of the roadway. The one or more controllers categorize the detection of a leading vehicle based on the perception data into one of a plurality of detection states, wherein the plurality of detection states indicate that the leading vehicle is currently falling out of the field of view of the perception sensors due to a geometry associated with the roadway. The one or more controllers classify a selected roadway profile and a selected detection state into one of a plurality of scenarios based on a set of classification rules and assign a specific vehicle following behavior based on a selected scenario.

[0006] According to another aspect, the leading vehicle is located immediately in front of the vehicle along the roadway, and wherein the rule-based adaptive cruise control system maintains a predefined forward travel distance between the vehicle and the leading vehicle.

[0007] According to yet another aspect, a longitudinal control command indicating when the vehicle accelerates and decelerates is based on vehicle following behavior.

[0008] According to one aspect, the set of classification rules is a set of conditional statements that classify the selected road profile and the selected detection state into the selected scenario.

[0009] According to another aspect, the road profiles include the following: a straight profile, a curved profile, an uphill profile, and a downhill profile.

[0010] According to yet another aspect, the straight profile indicates a flat road surface.

[0011] According to one aspect, the detection states include an undetected state, a detected state, and a detected but disappeared state.

[0012] According to another aspect, the detected but disappeared condition indicates that the preceding vehicle has currently left the field of view of the plurality of perception sensors while traveling along the roadway.

[0013] In yet another aspect, the vehicle following behavior instructs the vehicle to perform one of the following: following a target speed, adhering to a default behavior of an adaptive cruise control, reducing speed to negotiate a curved profile, following the leading vehicle according to a speed limit to negotiate the curved profile, negotiating the curved profile without sudden acceleration, negotiating an uphill profile without sudden acceleration, negotiating a downhill profile based on an allowable speed variation, and negotiating the downhill profile without sudden acceleration.

[0014] In one aspect, the plurality of perception sensors comprise one or more of the following: one or more cameras, an inertial measurement unit (IMU), a global positioning system (GPS), a radar, and a LiDAR.

[0015] According to another aspect, the rule-based adaptive cruise control system is part of an advanced driver assistance system (ADAS).

[0016] According to another aspect, a method for determining vehicle-following behavior of a vehicle by a rule-based adaptive cruise control system is disclosed. The method includes receiving, by one or more controllers, a route plan and perception data collected by a plurality of perception sensors, wherein the perception data represents an environment surrounding the vehicle, and the environment includes a roadway along which the vehicle is traveling. The method includes categorizing a profile of the roadway based on the route plan and the perception data into one of a plurality of roadway profiles, each of which indicates a geometry of the roadway.The method also includes categorizing the detection of a leading vehicle based on the perception data into one of a plurality of detection states, wherein the plurality of detection states indicate that the leading vehicle is currently falling out of a field of view of the perception sensors due to a geometry associated with the roadway. The method includes classifying a selected roadway profile and a selected detection state into one of a plurality of scenarios based on a set of classification rules. Finally, the method includes assigning a specific vehicle following behavior based on a selected scenario.

[0017] According to another aspect, a rule-based adaptive cruise control system for determining vehicle-following behavior of a vehicle is disclosed. The rule-based adaptive cruise control system includes a plurality of perception sensors for collecting perception data representing an environment surrounding the vehicle, wherein the environment includes a roadway along which the vehicle is traveling, and the perception sensors include a field of view. The one or more controllers are in electronic communication with the plurality of perception sensors. The one or more controllers execute instructions to receive a route plan and the perception data collected by the plurality of perception sensors.The one or more controllers execute instructions to categorize a profile of the roadway into one of a plurality of roadway profiles, each indicative of a geometry of the roadway, based on the route plan and the perception data. The one or more controllers categorize detection of a leading vehicle into one of a plurality of detection states based on the perception data, wherein the plurality of detection states indicate that the leading vehicle is currently falling out of view of the perception sensors due to a geometry associated with the roadway, and wherein the leading vehicle is located immediately ahead of the vehicle along the roadway, and wherein the rule-based adaptive cruise control system maintains a predefined headway distance between the vehicle and the leading vehicle.The one or more controllers classify a selected road profile and a selected detection condition into one of a plurality of scenarios based on a set of classification rules and assign a particular vehicle following behavior based on a selected scenario, wherein a longitudinal control command indicating when the vehicle accelerates and decelerates is based on the vehicle following behavior.

[0018] According to a further aspect, the set of classification rules is a set of conditional statements that classify the selected road profile and the selected detection state into the selected scenario.

[0019] According to yet another aspect, the roadway profiles include the following: a straight profile, a curved profile, an uphill profile, and a downhill profile.

[0020] According to one aspect, the straight profile indicates a level roadway.

[0021] According to a further aspect, the detection states include a non-detected state, a detected state, and a detected but disappeared state.

[0022] In one aspect, the detected but disappeared condition indicates that the preceding vehicle has currently left the field of view of the plurality of perception sensors while traveling along the roadway.

[0023] According to another aspect, the vehicle following behavior instructs the vehicle to perform one of the following: following a target speed, adhering to a default behavior of an adaptive cruise control, reducing speed to negotiate a curved profile, following the leading vehicle according to a speed limit to negotiate the curved profile, negotiating the curved profile without sudden acceleration, negotiating an uphill profile without sudden acceleration, negotiating a downhill profile based on a permissible speed variation, and negotiating the downhill profile without sudden acceleration.

[0024] In yet another aspect, the plurality of perception sensors comprise one or more of the following: one or more cameras, an IMU, a GPS, a radar, and a LiDAR.

[0025] Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for illustrative purposes only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are for illustrative purposes only and are in no way intended to limit the scope of the present disclosure; they show: Fig. 1 is a schematic diagram of a vehicle having the disclosed rule-based adaptive cruise control system with one or more controllers in electronic communication with a plurality of perception sensors according to an exemplary embodiment; Fig. 2A that in Fig. 1 traveling along a roadway and following a leading vehicle around a curve, according to an exemplary embodiment; Fig. 2B that in Fig. 1 following the preceding vehicle along an incline, according to an exemplary embodiment; Fig. 3 a representation of the software architecture of the one or more controllers used in Fig. 1, according to an exemplary embodiment; and Fig. 4 is a process flow diagram illustrating a method for determining vehicle following behavior by the rule-based adaptive cruise control system according to an exemplary embodiment. DETAILED DESCRIPTION

[0027] The following description is merely exemplary in nature and is not intended to limit the present disclosure, applications, or uses.

[0028] With reference to Fig. 1 is a schematic diagram of an exemplary rule-based adaptive cruise control system 10 for a vehicle 12. It will be appreciated that the vehicle 12 may be any type of vehicle, such as, but not limited to, a sedan, a truck, a sport utility vehicle, a van, or a motor home. In one embodiment, the rule-based adaptive cruise control system 10 is part of an advanced driver assistance system (ADAS) of the vehicle 12. The rule-based adaptive cruise control system 10 includes one or more controllers 20 in electronic communication with a plurality of perception sensors 22 configured to collect perception data indicative of an environment surrounding the vehicle 12. In the non-limiting embodiment, as shown in Fig. 1, the plurality of perception sensors 22 include one or more cameras 30, an inertial measurement unit (IMU) 32, a global positioning system (GPS) 34, a radar 36, and a LiDAR 38, however, it will be appreciated that additional sensors may be used as well. The one or more controllers 20 are also in electronic communication with one or more longitudinal controllers 24 that control the longitudinal motion of the vehicle 12. The one or more longitudinal controllers 24 may be, for example, a proportional-integral-derivative controller, a model predictive control (MPC) controller, or a sliding-mode controller.

[0029] As explained below, the one or more controllers 20 of the rule-based adaptive cruise control system 10 determine a vehicle following behavior 86 of the vehicle 12 based on the perception data collected by the plurality of perception sensors 22 and a route plan indicative of a selected route of the vehicle 12. The perception data collected by the plurality of perception sensors 22 indicates a detection condition of a preceding vehicle 50. The preceding vehicle 50 is located immediately in front of the vehicle 12 along the roadway 52 ( Fig. 2A and Fig. 2B), wherein the rule-based adaptive cruise control system 10 maintains a predefined forward travel distance between the vehicle 12 and the preceding vehicle 50. The detection state indicates that a preceding vehicle 50 is currently out of a field of view (FOV) of the perception sensors 22 ( Fig. 2A and Fig. 2B). The vehicle following behavior 86 is transmitted to the one or more longitudinal controllers 24. The one or more longitudinal controllers 24 determine a longitudinal control command 90 that instructs the vehicle 12 to accelerate or decelerate based on the vehicle following behavior 86.

[0030] Fig. 2A and Fig. 2B illustrate the vehicle 12 following a preceding vehicle 50 along a roadway 52, wherein the preceding vehicle 50, due to the geometry of the roadway 52, currently occupies the field of view FOV of the plurality of perception sensors 22 ( Fig. 1) without leaving lane 52. In the example, as shown in Fig. 2A, the geometry of the roadway 52 has a profile with a curve 54, wherein the preceding vehicle 50 leaves the field of view FOV of the plurality of perception sensors 22 while negotiating the curve 54 along the roadway 52. ​​In the example, as shown in Fig. 2B, the roadway 52 extends along an incline 56 and the leading vehicle 50 leaves the field of view FOV of the perception sensors 22 when a vehicle 12 travels up the incline 56 and the leading vehicle 50 travels down the incline 56.

[0031] Fig. 3 is an illustration of the architecture of the one or more controllers 20. The one or more controllers 20 include a perception module 60, a scenario classification module 62, and a behavior module 64. The perception module 60 includes a road profile sub-module 66 and a vehicle detection sub-module 68. With reference to both Fig. 1 and 3, the road profile sub-module 66 receives the route plan and the perception data collected by the plurality of perception sensors 22 representing the environment surrounding the vehicle 12, the environment being the roadway 52 ( Fig. 2A and Fig. 2B) along which the vehicle 12 is traveling. The vehicle detection sub-module 68 receives the perception data collected by the plurality of perception sensors 22, which represent the environment surrounding the vehicle 12.

[0032] With reference to Fig. 1, Fig. 2A and Fig. 2B, the road profile sub-module 66 of the perception module 60 categorizes a profile of the roadway 52 ( Fig. 2A and Fig. 2B) along which the vehicle 12 is currently traveling, based on the route plan and the perception data collected by the plurality of perception sensors 22, into one of a plurality of roadway profiles 80, each indicating the geometry of the roadway 52. ​​In the non-limiting embodiment, as shown in Fig. 3, the roadway profiles 80 include a straight-line profile 80A, a curved profile 80B, an uphill profile 80C, and a downhill profile 80D. The straight-line profile 80A indicates a level roadway, with the leading vehicle 50 constantly within the field of view (FOV) of the plurality of sensing sensors 22. In contrast, it can be seen that the curved profile 80B, the uphill profile 80C, and the downhill profile 80D indicate a profile that has the potential to cause the leading vehicle 50 to currently exit the field of view (FOV) of the plurality of sensing sensors 22 while still traveling along the roadway 52.

[0033] The vehicle detection sub-module 68 of the perception module 60 of the one or more controllers 20 categorizes the detection of the preceding vehicle 50 by the plurality of perception sensors 22 ( Fig. 1) into one of several detection states 82. In particular, the detection state 82 indicates that the preceding vehicle 50 is currently outside the field of view FOV of the perception sensors 22 due to the geometry of the roadway 52. ​​In the embodiment as shown in Fig. 3, the detection states 82 include a non-detected state 82A, which indicates that no preceding vehicle 50 is detected by the perception sensors 22, a detected state 82B, and a detected but disappeared state 82C. The detected but disappeared state 82C indicates a situation as shown in either Fig. 2A or Fig. 2B, wherein the leading vehicle 50 is currently leaving the field of view FOV of the plurality of perception sensors 22 as it travels along the roadway 52 due to the geometry of the roadway 52.

[0034] The scenario classification module 62 of the one or more controllers 20 receives a selected roadway profile 80 determined by the road profile sub-module 66 of the perception module 60 and a selected detection state 82 determined by the vehicle detection sub-module 68 of the perception module 60. The scenario classification module 62 of the one or more controllers 20 classifies the selected roadway profile 80 and the selected detection state 82 into one of a plurality of scenarios 84 based on a set of classification rules, wherein the set of classification rules defines the profile of the roadway 52 ( Fig. 2A and Fig. 2B) and the detection state of the preceding vehicle 50. Each scenario 84 corresponds to a specific vehicle following behavior 86 that indicates the operation of the vehicle 12, wherein the behavior module 64 assigns a specific vehicle following behavior 86 based on a selected scenario 84. The vehicle following behavior 86 is then sent to the longitudinal controllers 24, which in Fig. 1, wherein the one or more longitudinal controllers 24 determine the longitudinal control command 90.

[0035] The set of classification rules is a set of conditional statements or if-then rules that classify the selected roadway profile 80 determined by the road profile sub-module 66 of the perception module 60 and the selected detection state 82 determined by the vehicle detection sub-module 68 of the perception module 60 into a selected scenario 84. In the example, as in Fig. As shown in Figure 3, the scenario classification module 62 includes twelve different scenarios 84A-84L. Each scenario 84 corresponds to a specific vehicle following behavior 86.

[0036] In the example, as in Fig. 3, eight different vehicle following behaviors 86A-86H are shown. In one embodiment, vehicle following behavior 86A instructs vehicle 12 to follow a target speed, vehicle following behavior 86B instructs vehicle 12 to adhere to a default adaptive cruise control behavior, vehicle following behavior 86C instructs vehicle 12 to reduce speed to negotiate a curved profile, vehicle following behavior 86D instructs vehicle 12 to follow the leading vehicle 50 according to the speed limit to negotiate the curved profile, vehicle following behavior 86E instructs vehicle 12 to negotiate the curved profile without sudden acceleration, vehicle following behavior 86F instructs vehicle 12 to negotiate the uphill profile without sudden acceleration, vehicle following behavior 86G instructs vehicle 12to negotiate a downhill profile based on a permissible speed variation, and the vehicle following behavior 86H instructs the vehicle 12 to negotiate the downhill profile without sudden acceleration. The default behavior of the adaptive cruise control refers to the automatic adjustment of the vehicle speed to maintain a predefined forward travel distance or distance to the vehicle ahead. Sudden acceleration is triggered by a current loss of detection of the preceding vehicle 50 and refers to acceleration that falls outside an acceptable range, with the acceptable range of acceleration being selected based on occupant comfort. Sudden acceleration may result in system-controlled corrective braking,a driver intervention or driver discomfort from approaching the vehicle in front 50 or from aggressively negotiating a curve.

[0037] As an example, if the selected roadway profile 80 is the straight-line profile 80A and the selected detection state 82 is the undetectable state 82A, indicating that no leading vehicle 50 is detected by the perception sensors 22, the scenario classification module 62 of the one or more controllers 20 classifies the selected roadway profile 80 and the selected detection state 82 as a selected scenario 84A corresponding to the vehicle following behavior 86A that instructs the vehicle 12 to follow the target speed.As another example, when the selected roadway profile 80 is the curved profile 80B and the selected detection state 82 is the detected state 82B, the scenario classification module 62 of the one or more controllers 20 classifies the selected roadway profile 80 and the selected detection state 82 as a selected scenario 84E corresponding to the vehicle following behavior 86D that instructs the vehicle 12 to follow the leading vehicle 50 according to the speed limit to negotiate the curved profile.In yet another example, when the selected roadway profile 80 is the curved profile 80B and the selected detection state 82 is the detected but disappeared state 82C, the scenario classification module 62 of the one or more controllers 20 classifies the selected roadway profile 80 and the selected detection state 82 as a selected scenario 84F corresponding to the vehicle following behavior 86E that instructs the vehicle 12 to negotiate the curved profile without sudden acceleration.

[0038] Fig. 4 is a process flow diagram illustrating a method 400 for determining vehicle following behavior 86 by the rule-based adaptive cruise control system 10. With general reference to Fig. 1-4, the method 400 may begin at decision block 402. At decision block 402, the one or more controllers 20 continuously monitor the plurality of perception sensors 22 for the perception data. In response to receiving the perception data and the route plan, the method 400 proceeds to block 404.

[0039] In block 404, the road profile sub-module 66 of the perception module 60 of the one or more controllers 20 categorizes the profile of the roadway 52 (in Fig. 2A and Fig. 2B) into one of the plurality of roadway profiles 80, each indicating the geometry of the roadway 52. ​​The method 400 may then proceed to block 406.

[0040] At block 406, the vehicle detection sub-module 68 of the perception module 60 of the one or more controllers 20 categorizes the detection of the leading vehicle 50 into one of the plurality of detection states 82 based on the perception data, wherein the plurality of detection states indicate that the leading vehicle 50 is currently falling outside the field of view (FOV) of the perception sensors 22 due to a geometry associated with the roadway 52. ​​The method 400 may then proceed to block 408.

[0041] At block 408, the scenario classification module 62 of the one or more controllers 20 classifies the selected roadway profile 80 and the selected detection state 82 into one of a plurality of scenarios 84 based on a set of classification rules. The method 400 may then proceed to block 410.

[0042] In block 410, the behavior module 64 of the one or more controllers 20 assigns a particular vehicle following behavior 86 based on the selected scenario 84. The method 400 may then end.

[0043] With general reference to the figures, the disclosed rule-based adaptive cruise control system provides various technical effects and advantages. In particular, the disclosed rule-based adaptive cruise control system determines the following behavior of a host vehicle based on a set of classification rules that consider the road profile and a preceding vehicle detection state, resulting in a reduction in the occurrence of unnatural or sudden accelerations and decelerations that can occur with currently available adaptive cruise control systems. Reducing the occurrence of sudden accelerations and decelerations results in improved energy consumption and creates a more natural and comfortable driving experience for the vehicle's occupants.

[0044] The controllers may refer to or be a part of an electronic circuit, a combinational logic circuit, a field-programmable gate array (FPGA), a processor (shared, dedicated, or group) executing code, or a combination of some or all of the above, such as in a system on a chip. Furthermore, the controllers may be microprocessor-based, such as a computer with at least one processor, memory (RAM and / or ROM), and associated input and output buses. The processor may operate under the control of an operating system residing in memory. The operating system may manage computer resources so that computer program code embodied as one or more computer software applications, such as an application residing in memory, may include instructions executed by the processor.In an alternative embodiment, the processor may execute the application directly, in which case the operating system may be omitted.

[0045] The description of the present disclosure is merely exemplary in nature, and variations that do not depart from the gist of the present disclosure are intended to be within the scope of the present disclosure. Such variations should not be regarded as a departure from the spirit and scope of the present disclosure. legend

[0046] In the drawings, N stands for No and Y stands for Yes.

Claims

[1] A rule-based adaptive cruise control system for determining a vehicle following behavior of a vehicle, the rule-based adaptive cruise control system comprising: a plurality of perception sensors for collecting perception data representing an environment surrounding the vehicle, the environment including a roadway along which the vehicle is traveling, and the perception sensor having a field of view; one or more controllers in electronic communication with the plurality of perception sensors, the one or more controllers executing instructions to: Receiving a route plan and the perception data collected by the plurality of perception sensors; Categorizing a profile of the roadway based on the route plan and the perception data into one of several roadway profiles, each specifying a geometry of the roadway; Categorizing the detection of a preceding vehicle based on the perception data into one of a plurality of detection states, wherein the plurality of detection states indicate that the preceding vehicle is currently falling out of the field of view of the perception sensors due to a geometry associated with the roadway; Classifying a selected road profile and a selected detection state into one of several scenarios based on a set of classification rules; and Assigning a specific vehicle following behavior based on a selected scenario. [2] A rule-based adaptive cruise control system according to claim 1, wherein the preceding vehicle is immediately in front of the vehicle along the roadway and wherein the rule-based adaptive cruise control system maintains a predefined forward travel distance between the vehicle and the preceding vehicle. [3] A rule-based adaptive cruise control system according to claim 1, wherein a longitudinal control command indicating when the vehicle accelerates and decelerates is based on the vehicle following behavior. [4] A rule-based adaptive cruise control system according to claim 1, wherein the set of classification rules is a set of conditional statements that classify the selected road profile and the selected detection state into the selected scenario. [5] A rule-based adaptive cruise control system according to claim 1, wherein the road profiles include: a straight-line profile, a curved profile, an uphill profile, and a downhill profile. [6] A rule-based adaptive cruise control system according to claim 5, wherein the straight-line profile indicates a level roadway. [7] A rule-based adaptive cruise control system according to claim 1, wherein the detection states include a non-detected state, a detected state, and a detected but disappeared state. [8] The rule-based adaptive cruise control system of claim 1, wherein the detected but disappeared condition indicates that the preceding vehicle has currently left the field of view of the plurality of perception sensors while traveling along the roadway. [9] A rule-based adaptive cruise control system according to claim 1, wherein the vehicle following behavior instructs the vehicle to perform one of the following: following a target speed, adhering to a default behavior of an adaptive cruise control, reducing speed to negotiate a curved profile, following the leading vehicle according to a speed limit to negotiate the curved profile, negotiating the curved profile without sudden acceleration, negotiating an uphill profile without sudden acceleration, negotiating a downhill profile based on an allowable speed variation, and negotiating the downhill profile without sudden acceleration. [10] The rule-based adaptive cruise control system of claim 1, wherein the plurality of perception sensors comprises one or more cameras, an inertial measurement unit (IMU), a global positioning system (GPS), a radar, and / or a LiDAR. [11] A rule-based adaptive cruise control system according to claim 1, wherein the rule-based adaptive cruise control system is part of an advanced driver assistance system (ADAS). [12] A method for determining a vehicle following behavior of a vehicle by a rule-based adaptive cruise control system, the method comprising: Receiving, by one or more controllers, a route plan and perception data collected by a plurality of perception sensors, wherein the perception data represents an environment surrounding the vehicle, and the environment includes a roadway along which the vehicle is traveling; Categorizing a profile of the roadway based on the route plan and the perception data into one of several roadway profiles, each specifying a geometry of the roadway; Categorizing the detection of a leading vehicle based on the perception data into one of a plurality of detection states, wherein the plurality of detection states indicate that the leading vehicle is currently falling out of a field of view of the perception sensors due to a geometry associated with the roadway; Classifying a selected road profile and a selected detection state into one of several scenarios based on a set of classification rules; and Assigning a specific vehicle following behavior based on a selected scenario. [13] A rule-based adaptive cruise control system for determining a vehicle following behavior of a vehicle, the rule-based adaptive cruise control system comprising: a plurality of perception sensors for collecting perception data representing an environment surrounding the vehicle, the environment comprising a roadway along which the vehicle is traveling, and the perception sensors comprising a field of view; one or more controllers in electronic communication with the plurality of perception sensors, the one or more controllers executing instructions to: Receiving a route plan and the perception data collected by the plurality of perception sensors; Categorizing a profile of the roadway based on the route plan and the perception data into one of several roadway profiles, each specifying a geometry of the roadway; Categorizing the detection of a leading vehicle based on the perception data into one of a plurality of detection states, wherein the plurality of detection states indicate that the leading vehicle is currently falling out of the field of view of the perception sensors due to a geometry associated with the roadway, wherein the leading vehicle is immediately in front of the vehicle along the roadway, and wherein the rule-based adaptive cruise control system maintains a predefined forward travel distance between the vehicle and the leading vehicle; Classifying a selected road profile and a selected detection state into one of several scenarios based on a set of classification rules; and Assigning a specific vehicle following behavior based on a selected scenario, wherein a longitudinal control command indicating when the vehicle accelerates and decelerates is based on the vehicle following behavior. [14] A rule-based adaptive cruise control system according to claim 13, wherein the set of classification rules is a set of conditional statements that classify the selected road profile and the selected detection state into the selected scenario. [15] A rule-based adaptive cruise control system according to claim 13, wherein the road profiles comprise the following: a straight-line profile, a curved profile, an uphill profile and a downhill profile.

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