Method and system for speed control of driver in annular curve

Abstract

Methods and systems for providing driving assistance in a vehicle. In one embodiment, a method includes determining, by a processor, a trajectory of a vehicle along a roadway; determining, by a processor, a confidence score based on the predicted lateral acceleration and the actual lateral acceleration at the point along the trajectory; determining, by the processor, longitudinal velocity data based on the confidence score; and generating, by the processor, a control signal to a vehicle actuator to control the vehicle based on the longitudinal velocity data.

Description

[0001] TECHNICAL FIELD The present invention relates generally to systems, methods, and apparatus for providing longitudinal speed control by an autonomous or semi-autonomous driving system. Background Art

[0002] An autonomous vehicle is a vehicle that is able to sense its environment and navigate with little or no user input. Autonomous vehicles use sensing devices such as radar, lidar, image sensors, etc. to sense their environment. Autonomous vehicle systems also use information from global positioning system (GPS) technology, navigation systems, vehicle-to-vehicle communications, vehicle-to-infrastructure technology, and / or drive-by-wire systems to navigate the vehicle.

[0003] Vehicle automation has been categorized into numerical levels ranging from zero to five, with zero corresponding to no automation with full human control and five corresponding to full automation with no human control. Various automated driver assistance systems such as cruise control, adaptive cruise control, and parking assist systems correspond to lower levels of automation, while truly "driverless" vehicles correspond to higher levels of automation.

[0004] Some autonomous driving features support adaptive cruise control, lane following, lane centering, etc., where speed and / or steering are controlled to follow an intended path or intended lane. In some situations, such as on a curving road, the driving features may be disabled due to a lack of sensing capability of the road ahead.

[0005] Therefore, it is desirable to provide improved speed control strategies, methods and systems for improved autonomous or semi-autonomous control of vehicles.Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. Summary of the invention

[0006] Vehicles and methods and systems for providing driving assistance are disclosed herein. In one embodiment, a processor determines a trajectory of a vehicle along a road; the processor determines a confidence score based on a predicted lateral acceleration and an actual lateral acceleration at a point along the trajectory; the processor determines longitudinal velocity data based on the confidence score; and the processor generates a control signal to a vehicle actuator to control the vehicle based on the longitudinal velocity data.

[0007] In various embodiments, the method includes receiving, by a processor, sensor data from a sensor system of the vehicle, wherein the sensor data includes camera data, and wherein determining the trajectory is based on identifying lane markings on a road ahead of the vehicle from the camera data.

[0008] In various embodiments, when the lane marking is not identified from the camera data, determining the confidence score includes decreasing the confidence score.

[0009] In various embodiments, determining a confidence score is based on a difference between the predicted lateral acceleration and the actual lateral acceleration at a point along the trajectory, and when the difference is above a threshold, the confidence score is increased, and when the difference is below the threshold, the confidence score is decreased.

[0010] In various embodiments, the method includes determining a predicted lateral acceleration based on a radial point along the trajectory.

[0011] In various embodiments, the method includes determining an actual lateral acceleration based on the steering data.

[0012] In various embodiments, the point is a point along the trajectory that is in front of the vehicle.

[0013] In various embodiments, the point is an instantaneous point in the vehicle's path.

[0014] In various embodiments, determining the longitudinal speed data is based on a speed profile defined by a current speed, a set speed, and a desired speed.

[0015] In various embodiments, determining the longitudinal speed data is based on a comparison of the confidence score to a plurality of ranges, and selecting a speed profile determined from the plurality of speed profiles based on the comparison.

[0016] In another embodiment, a system includes a controller that includes a non-transitory computer-readable medium and is configured to, by a processor: determine a trajectory of a vehicle along a road; determine a confidence score based on predicted lateral acceleration and actual lateral acceleration at points along the trajectory; determine longitudinal velocity data based on the confidence score; and generate control signals to vehicle actuators to control the vehicle based on the longitudinal velocity data.

[0017] In various embodiments, the controller is further configured to receive sensor data from a sensor system of the vehicle, wherein the sensor data includes camera data, and wherein the controller determines the trajectory based on identifying lane markings on a road ahead of the vehicle from the camera data.

[0018] In various embodiments, when the lane marking is not identified from the camera data, the controller is configured to determine the confidence score by decreasing the confidence score.

[0019] In various embodiments, the controller is configured to determine a confidence score based on a difference between a predicted lateral acceleration and an actual lateral acceleration at a point along the trajectory, wherein when the difference is above a threshold, the controller is configured to increase the confidence score, and wherein when the difference is below the threshold, the controller is configured to decrease the confidence score.

[0020] In various embodiments, the controller is further configured to determine a predicted lateral acceleration based on a radial point along the trajectory.

[0021] In various embodiments, the controller is further configured to determine an actual lateral acceleration based on the steering data.

[0022] In various embodiments, the point is a point along the trajectory that is in front of the vehicle.

[0023] In various embodiments, the point is an instantaneous point in the vehicle's path.

[0024] In various embodiments, the controller is configured to: determine longitudinal speed data based on a plurality of speed profiles, each speed profile being defined by a current speed, a set speed, and a desired speed; determine the longitudinal speed data based on a comparison of a confidence score to a plurality of ranges; and select a speed profile from the plurality of speed profiles based on the comparison.

[0025] In another embodiment, a vehicle includes: a sensor system configured to sense the vehicle's environment; an actuator system; and a controller for implementing a driver assistance system, the controller configured to: determine a trajectory of the vehicle along a road; determine a confidence score based on predicted lateral acceleration and actual lateral acceleration at points along the trajectory; determine longitudinal velocity data based on the confidence score; and generate a control signal to the actuator system to control the vehicle based on the longitudinal velocity data. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Exemplary embodiments will be described below with reference to the following drawings, wherein like reference numerals denote like elements, and wherein:

[0027] Figure 1 is a block diagram illustrating an autonomous vehicle with a speed control system according to various embodiments;

[0028] Figure 2 is a functional block diagram illustrating components of an autonomous driving system having a speed control system according to various embodiments;

[0029] Figure 3 is a data flow diagram illustrating features of a speed control system for a vehicle according to various embodiments;

[0030] Figure 4 is a graphical representation of roads and trajectories and data determined by a speed control system according to various embodiments; and

[0031] Figure 5 is a process flow diagram illustrating an example process of speed control according to various embodiments. DETAILED DESCRIPTION

[0032] The following detailed description is merely exemplary in nature and is not intended to limit application and use. In addition, it is not intended to be bound by any express or implied theory presented in the previous technical field, background technology, invention content or the following detailed description. As used herein, the term "module" refers to any hardware, software, firmware, electronic control component, processing logic and / or processor device alone or in any combination, including but not limited to: application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), electronic circuits, processors (shared, dedicated or groups) and memories that execute one or more software or firmware programs, combinational logic circuits and / or other suitable components that provide the described functions.

[0033] Embodiments of the present disclosure may be described herein in terms of functional and / or logical block components and various processing steps. It should be understood that these block components may be implemented by any number of hardware, software and / or firmware components configured to perform a specified function. For example, embodiments of the present disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of the present disclosure.

[0034] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, lidar, image analysis, and other functional aspects of the system (and the various operating components of the system) may not be described in detail herein. In addition, the connecting lines shown in the various figures included herein are intended to represent example functional relationships and / or physical connections between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of the present disclosure.

[0035] refer to Figure 1 According to various embodiments, a speed control system, generally shown at 100, is associated with a vehicle 10. Generally, the speed control system 100 controls the longitudinal speed of the vehicle using velocity profiling based on a determined confidence score. For example, the speed control system 100 determines the confidence score by comparing the driver action (i.e., the current vehicle path based on, for example, the road wheel angle) to the planned optimal trajectory. In many cases, the speed control system 100 uses the confidence score to perform velocity profiling even when road signs disappear.

[0036] like Figure 1As shown in FIG. 1 , the vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially surrounds the components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. The wheels 16 to 18 are each rotatably coupled to the chassis 12 near a corresponding corner of the body 14.

[0037] In various embodiments, the vehicle 10 is an autonomous or semi-autonomous vehicle, and the speed control system 100 is incorporated into the autonomous or semi-autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). For example, the autonomous vehicle 10 is a vehicle that is automatically controlled in some manner to transport passengers or cargo from one location to another. In the illustrated embodiment, the vehicle 10 is shown as a passenger car, but it should be understood that any other means of transportation may also be used, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc. In an exemplary embodiment, the autonomous vehicle 10 is configured to perform autonomous features, such as, but not limited to, hands-on lane centering, adaptive cruise control, super cruise, super cruise, etc.

[0038] As shown, the autonomous vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, the propulsion system 20 may include an internal combustion engine, an electric motor (such as a traction motor), and / or a fuel cell propulsion system. The transmission system 22 is configured to transfer power from the propulsion system 20 to the wheels 16 to 18 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a stepped automatic transmission, a continuously variable transmission, or other suitable transmission. The braking system 26 is configured to provide braking torque to the wheels 16 to 18. In various embodiments, the braking system 26 may include a friction brake, a wire control brake, a regenerative braking system (such as a motor), and / or other suitable braking systems. The steering system 24 affects the position of the wheels 16 to 18.

[0039] The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external environment and / or the internal environment of the autonomous vehicle 10. The sensing devices 40a-40n may include, but are not limited to, radar, lidar, global positioning system, optical camera, thermal camera, ultrasonic sensor, and / or other sensors. The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may also include interior vehicle features and / or exterior vehicle features, such as, but not limited to, doors, trunks, and cabin features, such as air, music, lighting, etc. (not numbered).

[0040] The communication system 36 is configured to wirelessly transmit information to and from other entities 48, such as, but not limited to, other vehicles ("V2V" communications), infrastructure ("V2I" communications), remote systems, and / or personal devices (for example, Figure 2 In an exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or by using cellular data communications. However, additional or alternative communication methods, such as dedicated short-range communication (DSRC) channels, are also considered within the scope of the present disclosure. A DSRC channel refers to a one-way or two-way short-range to medium-range wireless communication channel designed specifically for automotive use and a corresponding set of protocols and standards.

[0041] Data storage device 32 stores data for automatically controlling autonomous vehicle 10. In various embodiments, data storage device 32 stores a defined map of a navigable environment. In various embodiments, the defined map may be predefined by a remote system and obtained from the remote system. For example, the defined map may be assembled by a remote system and transmitted to autonomous vehicle 10 (wirelessly and / or in a wired manner) and stored in data storage device 32. It will be appreciated that data storage device 32 may be part of controller 34, separate from controller 34, or part of controller 34 and part of a separate system.

[0042] The controller 34 includes at least one processor 44 and a computer readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), a secondary processor in a number of processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. For example, the computer readable storage device or medium 46 can include volatile and non-volatile storage in read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a permanent or non-volatile memory that can be used to store various operating variables when the processor 44 is powered off. The computer readable storage device or medium 46 can be implemented using any of a variety of known memory devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination memory device capable of storing data, some of which represents executable instructions used by the controller 34 in controlling the autonomous vehicle 10.

[0043] The instructions may include one or more separate programs, each of which includes an ordered list of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals from sensor system 28, perform logic, calculations, methods, and / or algorithms for automatically controlling components of autonomous vehicle 10, and generate control signals to actuator system 30 to automatically control components of autonomous vehicle 10 based on the logic, calculations, methods, and / or algorithms. Although in Figure 1 Only one controller 34 is shown, but embodiments of autonomous vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication mediums and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control features of autonomous vehicle 10.

[0044] In various embodiments, as discussed in detail below, one or more instructions of controller 34 are embodied in speed control system 100 and, when executed by processor 44, process sensor data and / or other data, predict an optimal trajectory, compare driver actions to the optimal trajectory, calculate a confidence score based on the comparison, and shape a longitudinal response profile for vehicle 10 based on the confidence score.

[0045] Reference now Figure 2 , and continue to refer to Figure 1, a data flow diagram illustrates various embodiments of an autonomous driving system (ADS) 70 that may be embedded within the controller 34 and may include portions of the speed control system 100 according to various embodiments. That is, appropriate software and / or hardware components of the controller 34 (e.g., the processor 44 and the computer-readable storage device 46) are used to provide the autonomous driving system 70 for use in conjunction with the vehicle 10.

[0046] Inputs to autonomous driving system 70 may be received from sensor system 28, from other control modules (not shown) associated with autonomous vehicle 10, from communication system 36, and / or determined / modeled by other submodules (not shown) within controller 34. In various embodiments, instructions to autonomous driving system 70 may be organized by function or system. For example, Figure 2 As shown in FIG, the autonomous driving system 70 may include a computer vision system 74, a positioning system 76, a guidance system 78, and a vehicle control system 80. It will be appreciated that in various embodiments, instructions may be organized into any number of systems (e.g., combined, further segmented, etc.), as the present disclosure is not limited to the current example.

[0047] In various embodiments, the computer vision system 74 integrates and processes the sensor data and predicts the presence, location, classification, and / or path of objects and characteristics of the environment of the vehicle 10. In various embodiments, the computer vision system 74 may combine information from multiple sensors, including but not limited to cameras, lidar, radar, and / or any number of other types of sensors.

[0048] The positioning system 76 processes the sensor data and other data to determine the position of the vehicle 10 relative to the environment (e.g., local position relative to a map, precise position relative to a road lane, vehicle heading, speed, etc.). The guidance system 78 processes the sensor data and other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals for controlling the vehicle 10 based on the determined path. Specifically, the vehicle control system 80 generates control signals (e.g., steering control signals, acceleration control signals, braking control signals) for the actuator system 30 to guide the vehicle 10 to follow the desired trajectory determined by the guidance system 78.

[0049] As briefly described above, Figure 1 The speed control system 100 is included in the autonomous driving system 70. For example, all or part of the speed control system 100 can be included in one or more of the subsystems, or as a separate system (as shown). Figure 3 More details are shown and reference is made to Figure 1 and Figure 2The speed control system 100 includes a trajectory determination module 350 , a lateral acceleration determination module 352 , a confidence score determination module 354 , a longitudinal control module 356 , and a speed profile data repository 358 .

[0050] In various embodiments, the speed profile data repository 358 stores speed profiles, which define acceleration or deceleration modes for longitudinal control of the vehicle 10. In various embodiments, any number of speed profiles may be defined. For example, they may be defined as a multidimensional lookup table based on current speed, set speed, and desired speed or speed error. In various embodiments, the speed profile provides a torque value as an output to drive the wheel at a desired speed. The torque value may be defined according to the configuration of the propulsion system, transmission system, and / or braking system 26 of the vehicle 10 and according to the resulting deceleration rate / acceleration rate. For exemplary purposes, the present disclosure will be discussed based on at least three exemplary speed profiles with three different rates, the first profile being referred to as the optimal acceleration / deceleration profile, the second profile being referred to as the partially limited acceleration / deceleration profile, and the third profile being referred to as the limited acceleration / deceleration profile.

[0051] In various embodiments, the trajectory determination module 350 receives sensor data 360 as input. The trajectory determination module 350 processes the sensor data 360 to determine an upcoming trajectory of the vehicle 10. In various embodiments, the trajectory determination module 350 determines the trajectory based on the type and location of lane markings identified in front of the vehicle 10. For example, the sensor data 360 may include camera data from a camera sensor. It will be appreciated that in various embodiments, other sensor data may be used to determine lane markings.

[0052] The trajectory determination module 350 processes the camera data using one or more machine learning techniques to extract the type and location of any left and / or right lane markings in front of the vehicle 10. In various embodiments, the machine learning techniques provide a confidence score as well as the type and location, and the trajectory determination module 350 determines a trajectory based on the type and location of the lane markings and the confidence score. Thereafter, the trajectory determination module 350 identifies one or more radial points along the trajectory. For example, Figure 4 As shown in , the trajectory determination module 350 determines radial points (r0, r1, r2, etc.) along the predicted trajectory 390 (e.g., by sweeping the trajectory 390) based on the following relationship:

[0053]

[0054] Where y(x) = c 0 +c 1 x+c 2 x 2 +c 3 x3 , and wherein la means looking ahead.

[0055] The trajectory determination module 350 generates look-ahead point data 362 based on the determined points.

[0056] In some cases, the trajectory determination module 350 does not identify any lane markers, or the trajectory determination module 350 cannot identify lane markers with a certain confidence level, and the trajectory determination module 350 determines that a small distance (e.g., less than a threshold) has passed since the last lane marker was identified. In these cases, the trajectory determination module 350 determines an instantaneous point along the current path of the vehicle 10 based on the last known lane marker or the projected virtual lane marker, and generates instantaneous point data 363 based thereon.

[0057] In various embodiments, the lateral acceleration determination module 352 receives as input the look-ahead point data 362 or the instantaneous point data 363 and the sensor data 364. The sensor data 364 may include steering data from a sensor associated with a steering system. When the look-ahead point data 362 is received, the lateral acceleration determination module 352 predicts the lateral acceleration at the look-ahead point identified by the look-ahead data 362.

[0058] For example, lateral acceleration can be predicted based on the following relationship:

[0059]

[0060] Where r represents the curvature at that point, and V x (t) represents the longitudinal velocity at time t.

[0061] In various embodiments, the speed may be calculated based on the following relationship:

[0062]

[0063] Among them, a x represents the longitudinal acceleration, and wherein C represents the compensation value determined from the currently applied speed profile. For example, the lateral acceleration determination module 352 determines whether any compensation for lateral acceleration is required based on the longitudinal speed profile being executed. For example, if the current longitudinal speed profile requires longitudinal deceleration due to another vehicle being detected in the lane, the predicted lateral acceleration is compensated to account for the expected deceleration of the vehicle 10. It will be appreciated that the compensation may be based on other scenarios and profiles, as the present disclosure is not limited to this example.

[0064] In various embodiments, when the instantaneous point data 363 is received, the lateral acceleration determination module 352 predicts the lateral acceleration at the instantaneous point identified by the instantaneous point data 363. It will be appreciated that the predicted lateral acceleration may be similarly determined and compensated based on the longitudinal velocity profile as discussed above.

[0065] In various embodiments, the lateral acceleration determination module 352 then determines, at the look-ahead or instantaneous point, the actual lateral acceleration resulting from the driver steering the vehicle 10. For example, the sensor data 364 may include steering data from a sensor associated with the steering system 24 of the vehicle 10. The lateral acceleration determination module 352 evaluates the steering data and generates actual lateral acceleration data 370 based thereon.

[0066] In various embodiments, the confidence score determination module 354 receives as input the predicted lateral acceleration data 368 and the actual lateral acceleration data 370. The confidence score determination module 354 determines a confidence score and based thereon generates confidence score data 372. For example, when the predicted lateral acceleration is based on an instantaneous point, the confidence score determination module 354 reduces the confidence score.

[0067] In another example, when the predicted lateral acceleration is based on the trajectory, then the confidence score determination module 354 calculates the difference between the predicted lateral acceleration data 368 and the actual lateral acceleration data 370, and evaluates the difference to calculate the confidence score. For example, when the difference is greater than a threshold, the confidence score determination module 354 decreases the confidence score, and the confidence score data 372 is generated based thereon. In another example, when the difference is less than or equal to the threshold, the confidence score determination module 354 increases the confidence score, and the confidence score data 372 is generated based thereon.

[0068] The longitudinal control module 356 receives as input the confidence score data 372. Based on the confidence score, the longitudinal control module 356 selects a longitudinal speed profile from the speed profile data repository 358 by which to control the longitudinal speed of the vehicle 10. The longitudinal control module 356 then generates speed control data 376, which is used to control the vehicle 10 based on the selected speed profile.

[0069] For example, when the confidence score is within a first range (e.g., a high range), the longitudinal speed profile shapes the speed according to an acceleration / deceleration rate based on an optimal speed profile. When the confidence score is within a second range (e.g., a medium range), the longitudinal speed profile shapes the speed according to a partially restricted speed profile. When the confidence score is within a third range (e.g., a low range), the longitudinal speed profile shapes the speed according to a restricted speed profile. It will be appreciated that in various embodiments, any number of ranges may be implemented with any number of speed profiles.

[0070] Reference now Figure 5 , and continue to refer to Figures 1 to 4 , the flow chart shows that according to the present disclosure, Figures 1 to 4 The process 400 is performed by the speed control system 100. It can be understood from the present disclosure that the order of operations in the process 400 is not limited to the following. Figure 5 The process 400 is not performed in the order shown, but may be performed in one or more different orders as applicable and in accordance with the present disclosure. In various embodiments, the process 400 may be scheduled to run based on one or more predetermined events, and / or may be run continuously during operation of the autonomous vehicle 10, and / or may be run offline prior to operation of the vehicle 10.

[0071] In one example, process 400 may begin at 405. At 410, it is determined whether longitudinal control is active, such as by enabling an autonomous or semi-autonomous feature. If longitudinal control is not active at 410, process 400 may continue to monitor when longitudinal control is active or may end at 410. When it is determined at 410 that longitudinal control is active, at 420, it is determined whether a lane marker is identified (e.g., from camera data) with sufficient confidence (e.g., above a threshold), or whether the distance since the last seen lane marker is below a threshold.

[0072] When the lane marking is identified as having a threshold confidence at 420, an optimal trajectory is determined based on the lane marking at 430. At 440, lateral acceleration is predicted at one or more look-ahead points along the trajectory. At 450, the predicted lateral acceleration is compensated based on any active speed profile. At 460, an observed lateral acceleration is determined at the look-ahead point based on the actual vehicle path and / or steering data.

[0073] Thereafter, at 470, the difference between the observed lateral acceleration and the expected lateral acceleration is calculated and compared to a threshold value. When the difference at 470 is greater than the threshold value, at 480, the confidence score is decreased. When the difference at 470 is less than or equal to the threshold value, at 490, the confidence score is increased.

[0074] When the lane marker is not identified as having a threshold confidence or the distance since the lane marker was lost is less than the threshold at 420, at 500, it is determined whether the speed profile is active. If the speed profile is not active at 500, then process 400 continues at 410. If the speed profile is active at 500, then at 510, the confidence score is reduced. At 520, the lateral acceleration at the instantaneous data point is determined based on the current path of the vehicle and is optionally compensated based on the active speed profile. At 460, an observed lateral acceleration is determined at the instantaneous data point based on the actual vehicle path and / or steering data.

[0075] Once the confidence score is determined at 480, 490 or 510, process 400 proceeds to 530, where the confidence score is compared to the three ranges to determine the appropriate speed profile to be enabled. For example, when the confidence score is high or in the high range, the longitudinal speed is shaped according to the optimal speed profile. When the confidence score is medium or in the medium range, the longitudinal speed is shaped according to the partially restricted speed profile. When the confidence score is small or in the small range, the longitudinal speed is shaped according to the restricted speed profile. Thereafter, process 400 can end or return to the beginning at 410.

[0076] Although at least one exemplary embodiment has been proposed in the foregoing detailed description, it should be understood that there are a large number of variations. It should also be understood that one or more 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 a convenient roadmap for implementing one or more exemplary embodiments for those skilled in the art. It should be understood that various changes may be made to the functions and arrangements of the elements without departing from the scope of the present disclosure as set forth in the attached claims and their legal equivalents.

Claims

1. A method for providing driving assistance in a vehicle, comprising: determining, by a processor, a trajectory of the vehicle along a road; determining, by the processor, a confidence score based on the predicted lateral acceleration and the actual lateral acceleration at the point along the trajectory; determining, by the processor, longitudinal velocity data based on the confidence score; as well as Control signals are generated by the processor to vehicle actuators to control the vehicle based on the longitudinal velocity data.

2. The method according to claim 1, further comprising: Sensor data is received, by the processor, from a sensor system of the vehicle, wherein the sensor data includes camera data, and wherein the determining the trajectory is based on identifying lane markings on a road ahead of the vehicle from the camera data.

3. The method according to claim 2, wherein: When the lane marking is not identified from the camera data, determining the confidence score includes decreasing the confidence score.

4. The method according to claim 1, wherein: The determining of the confidence score is based on a difference between the predicted lateral acceleration and the actual lateral acceleration at the point along the trajectory, wherein when the difference is above a threshold, the confidence score is increased, and wherein when the difference is below the threshold, the confidence score is decreased. 5 . The method of claim 1 , further comprising determining the predicted lateral acceleration based on a radial point along the trajectory. The method of claim 1 , further comprising determining the actual lateral acceleration based on steering data.

7. The method according to claim 1, wherein: The point is a point along the trajectory that is in front of the vehicle.

8. The method according to claim 1, wherein: The points are instantaneous points in the vehicle's path.

9. The method according to claim 1, wherein: The determining of the longitudinal speed data is based on a speed profile defined by a current speed, a set speed, and a desired speed.

10. A system for providing driving assistance in a vehicle, comprising: A controller comprising a non-transitory computer readable medium and configured, by a processor: determining a trajectory of the vehicle along a road; determining a confidence score based on the predicted lateral acceleration and the actual lateral acceleration at the point along the trajectory; determining longitudinal velocity data based on the confidence score; as well as Control signals are generated to vehicle actuators to control the vehicle based on the longitudinal velocity data.

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