Systems and methods for controlling the speed of vehicles

Abstract

Cruise control system (20), comprising: a position determination sensor (34) configured to obtain a set of locations (s) along a road (R); a distance sensor (32) configured to measure a distance (dt) to one or more nearby vehicles (10t); a speed sensor designed to measure a speed (v t ) to measure from one or more nearby vehicles (10t); a processor (50); and a memory (52) containing instructions executable by a computer, comprising: a speed optimization application (60) which, when executed by the processor (50), causes the processor (50) to determine a speed profile (v(s)), wherein the speed profile (v(s)) comprises a set of speeds (v) corresponding to the set of digits (s), wherein the speed profile (v(s)) is determined as the minimum from: a first subexpression representing a time to travel through the set of places (s), a second subexpression representing a longitudinal acceleration of the vehicle (10) at the set of positions (s); a third subexpression representing a lateral acceleration of the vehicle (10) at the set of points (s); and a fourth subexpression representing an accumulated deviation from a traffic flow speed; wherein the speed control system (20) further comprises a selection application (64) which includes computer-executable instructions which, when executed by the processor (50), cause the processor (50) to: indicates the presence of a nearby vehicle (10t) that is closest on the track when at least a distance (dt) to a nearby vehicle (10t) measured by the distance sensor (32) is less than a distance threshold (dr), and selects a speed command from an application (62) for adaptive cruise control for a throttle / brake controller (22); and otherwise, it indicates the absence of a nearby vehicle (10t) that is closest on the track and selects a speed command from the speed optimization application (60) for the throttle / brake controller (22).

Description

TECHNICAL AREA

[0001] The technical field generally concerns systems and methods for controlling a vehicle, and in particular systems and methods for controlling the speed of vehicles. BACKGROUND

[0002] Current cruise control systems are not designed to handle various situations, including entering a curve, exiting a curve, navigating a curve, and encountering oncoming traffic. For example, some current cruise control systems operate adaptive cruise control and cornering cruise control simultaneously, with a decision module selecting between the two. Such cruise control systems may exhibit jerkiness during the transition between the two control modes.

[0003] The publication DE 10 2008 039 950 A1 discloses a method for determining a driving profile for road vehicles, which calculates a speed profile along a determined route taking into account a topology of the route, a distance of a vehicle ahead and a traffic flow.

[0004] German patent application DE 103 45 319 A1 discloses a predictive speed control system for a motor vehicle, in which a vehicle operating cost function is optimized during operation by calculating suitable vehicle control parameters. An optimal speed determined in this process is adjusted if a current speed cannot be maintained due to a slow-moving vehicle ahead. SUMMARY

[0005] The various embodiments overcome the shortcomings of the prior art. The systems and methods described here provide a cruise control system that automatically and optimally adapts to different situations while maintaining driver comfort. These situations include navigating a curve and approaching an oncoming vehicle. An exemplary cruise control system generally comprises an application that combines cornering cruise control, speed limit control, and adaptive cruise control to generate an optimized speed profile used to control the vehicle.For example, the speed profile is a function of locations along a route, of steering courses at those locations, of a speed limit at those locations, and of the distance to and speed of one or more nearby vehicles.

[0006] The foregoing has outlined some aspects and features of the various embodiments in broad strokes, and these should be considered merely as illustrations of various potential applications. Other advantageous results may be obtained by applying the disclosed information in a different manner or by combining different aspects of the disclosed embodiments. Further aspects and a more comprehensive understanding can be obtained by referring to the detailed description of the exemplary embodiments in conjunction with the accompanying drawings, in addition to the scope defined by the claims. DETAILED DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a top view of a vehicle and a nearby vehicle, both traveling along a road, according to an exemplary embodiment. Fig. Figure 2 is a schematic view of the vehicle from Fig. 1, which represents a speed control system. Fig. Figure 3 is a top view of the street from Fig. 1, which represents the locations on the road and tax rates at the locations. Fig. Figure 4 is a graphical representation of the course and curvature at the points of Fig. 3. Fig. Figure 5 is a graph used to determine a velocity profile, where the graph represents a set of velocities for each of the positions of Fig. Contains 3. Fig. Figures 6-8 show the graph of Fig. 5 with steps of an exemplary procedure for determining a speed profile. Fig. Figure 9 is a graphical illustration of an example speed profile. Fig. Figure 10 is a diagram of an exemplary procedure for selecting an application. DETAILED DESCRIPTION

[0007] As required, specific embodiments are disclosed herein. It is understood that the disclosed embodiments are only examples of various and alternative forms. The word "exemplary," as used here, is employed extensively to denote embodiments that serve as illustrations, samples, models, or templates. The figures are not necessarily to scale, and some features may be emphasized or minimized to show details of specific components. In other cases, well-known components, systems, materials, or processes known to those skilled in the art have not been described in detail to avoid obscuring the present disclosure. Therefore, specific structural and functional details disclosed herein should not be considered as limitations, but only as a basis for the claims and as a representative basis for instructing those skilled in the art.

[0008] With reference to Fig. Figures 1-3 describe a vehicle 10 comprising a cruise control system 20, which is designed to automatically regulate the speed v of the vehicle 10 at points s along a road R that contains curves X. For example, the cruise control system 20 provides a command to a throttle / brake controller 22 of the vehicle. The throttle / brake controller 22 is designed to control a throttle system 24 of the vehicle 10 and a brake system 26 of the vehicle 10.

[0009] The cruise control system 20 includes a speed controller 30 configured to generate a command for the throttle / brake controller 22. For illustrative purposes, a single controller is described. However, in some embodiments, multiple controllers are used. For example, a first controller determines a first command using a first application, a second controller determines a second command using a second application, and the cruise control system selects which command is forwarded to the throttle / brake controller 22. Furthermore, in some embodiments, the throttle / brake controller 22 is integrated into the cruise control system 20.

[0010] The cruise control system 20 also includes sensors designed to measure data and input that data into the speed controller 30. The cruise control system 20 also includes a speed and distance sensor, such as a radar distance sensor 32, designed to measure a speed v t The radar distance sensor 32 measures the speed of one or more nearby vehicles 10t and the distance dt from them. In alternative embodiments, several sensors independently measure the speed of and distance to nearby vehicles and deliver measurement data to a processor. The radar distance sensor 32 has a range, and a distance threshold dr is selected within this range. For example, the distance threshold dr can be one hundred and twenty meters, or the maximum detection range of the radar distance sensor 32.

[0011] The cruise control system 20 includes a position sensor, such as a sensor 34 of a global positioning system (GPS sensor) (i.e., a GPS device), which is configured to determine, among other things, positions s, the heading θ(s) of the vehicle 10 at positions s on curve X, the current speed v0, and the current position s0. For example, the GPS device 34 stores a digital map 36 or accesses it wirelessly to determine the positions s, the heading angles θ(s), a speed limit v L (s) and other information to determine (see Fig. 3 and Fig. 4) For example, the speed limit is the speed limit set by the government for the area or similar.

[0012] In some embodiments, the cruise control system 20 includes a longitudinal speed sensor (e.g., vehicle dynamics sensors or wheel pulse generators) configured to measure the current speed v0 of the vehicle 10. Alternatively, the functions of the GPS device are at least partially performed by the cruise control system's processor. For example, the memory 52 stores the steering headings, which are accessed in accordance with the position determined by the GPS sensor 34.

[0013] With reference to Fig. 2 The vehicle speed controller 30 is generally configured to receive input from sensors 32, 34 and to generate a command for the throttle / brake controller 22 to regulate the speed of the vehicle 10. The controller 30 includes a processor 50 and a specific computer-readable medium or memory 52 that stores instructions executable by a computer for carrying out the procedures described herein. The term "computer-readable media" and variants thereof, as used in the description and the claims, refer to mass storage media.In some embodiments, mass storage media include volatile and / or non-volatile, replaceable and / or non-replaceable media, such as random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), semiconductor memory or other storage technology, CD-ROM, DVD, BLU-RAY or other optical disk storage, magnetic tape, magnetic disk mass storage or other magnetic mass storage devices.

[0014] Although the methods described herein may sometimes be described in a general context of computer-executable instructions, the methods of this disclosure may also be implemented in combination with other applications and / or as a combination of hardware and software. The term "application" or variants thereof is used extensively here to encompass routines, program modules, programs, components, data structures, algorithms, and the like. Applications may be implemented in various system configurations, including servers, network systems, systems with one or more processors, minicomputers, mainframe computers, personal computers, portable computing devices, mobile devices, microprocessor-based programmable consumer electronics, combinations thereof, and the like.

[0015] Memory 52 contains a speed optimization application 60, which is designed to coordinate adaptive cruise control, cornering cruise control, and speed limit control. The speed optimization application 60 uses the input from sensors 32 and 34 to generate the speed profile v(s) in an optimal manner and generates a speed command based on the speed profile v(s).

[0016] Memory 52 also contains an adaptive cruise control application 62, which is designed to implement adaptive cruise control. The adaptive cruise control application 62 uses an input from the radar distance sensor 32 to generate a speed command as a function of the nearest vehicle 10t on the track. The nearest vehicle 10t on the track is one of the nearby vehicles 10t, is closest to vehicle 10, and is closer than the distance threshold dr.

[0017] Memory 52 also contains a selection application 64, which is configured to choose between the speed command of the speed optimization application 60 and the speed command of the adaptive cruise control application 62. The selected speed command is sent to the throttle / brake controller 22.

[0018] Adaptive cruise control, cornering cruise control, and speed limit control are now described in more detail. Adaptive cruise control is a regulation of the speed v of vehicle 10 relative to one or more nearby vehicles 10t on the road R close to vehicle 10 (as in Fig. (as shown in Figure 1). To perform adaptive cruise control, the radar distance sensor 32 or another vehicle device measures the speed v. t of and the distance dt to one or more nearby vehicles 10t. A target flow velocity v tf 10t can be determined from measurements of several nearby vehicles, as described in further detail below.

[0019] Cruise control during cornering is the regulation of the speed v of vehicle 10 while the vehicle 10 is moving through a curve X. To perform cruise control during cornering, the device 34 of the global positioning system (GPS device) determines positions s on curve X and the steering heading θ(s) of vehicle 10 at these positions s on curve X.

[0020] The speed limit control is a regulation of the speed v of vehicle 10 relative to the speed limit v. L To implement the speed limit control, the GPS device 34 determines the speed limit v L (s) at the points s along the road R. Speed ​​optimization application

[0021] According to an exemplary embodiment, the speed optimization application contains 60 computer-executable instructions for minimizing the following expression: minv(s)∫0s{C11v(s)+C2|v(s)dv(s)ds|+C3[k(s)(v(s)2−vL(s)2)]+C4H(v(s))}ds and to meet the following restrictions: |dv(s)ds|≤am and |v(s)−vL(s)|≤ε to determine the velocity profile v(s) for a set of points s∈[0,S], which is represented by the arc length parameter S. Alternatively, equation 3 can be used. v(s)≤vL(s)+ε, to be something that sets an upper limit.

[0022] Equation 1 is described in further detail below. Considering the limitations of Equations 2 and 3, the maximum acceleration a m An acceleration value that represents a performance capacity of the vehicle 10. For example, the maximum acceleration am In one embodiment, forty percent of the gravitational acceleration is used for positive acceleration and sixty percent for deceleration. The tolerance ε is an acceptable deviation from the speed limit v. L For example, the tolerance ε might be 16 km / h (ten miles per hour) on highways and 8 km / h (five miles per hour) on rural roads. The tolerance ε is typically a relatively small positive number.

[0023] Equation 1 contains subexpressions (equations 4, 5, 6, and 8 below) representing the relevant factors, including the desire to travel quickly along road R, driver comfort, adherence to the speed limit, and the desire to maintain the same speed as the detected speed of the traffic flow. The subexpressions are weighted according to their relative importance by constants C1, C2, C3, and C4.

[0024] The coefficients C1, C2, C3, and C4 in Equation 1 are weighting factors that can be calibrated using test runs. These coefficients determine the weight of the subexpressions during the minimization of Equation 1. The larger the weighting factor, the greater the contribution of the corresponding subexpression to the minimization of Equation 1. For clarity, the coefficients C1, C2, C3, and C4 are either set to one or otherwise omitted from the subexpressions.

[0025] The first subexpression ∫0s1v(s)ds is the time to go through the set of places s∈[0,S], to pass through, which is minimized by maximizing the velocity profile v(s).

[0026] The second subexpression ∫0s|v(s)dv(s)ds|ds is the accumulated longitudinal acceleration through the set of points s∈[0,S], which is minimized by maintaining a constant velocity v.

[0027] The third subexpression ∫0Sk(s)(v(s)2−vL(s)2)ds is the accumulated lateral acceleration. With reference to Fig. 3 and Fig. 4. The curvature k(s) is given as k(s)=|dθ(s)ds| and is therefore a function of the heading angle θ(s). The heading angle θ(s) is the angle to the line that is tangent to the road R at point s. The given legal speed limit v L (s) is derived from the digital map 36. The third subexpression is zero along a straight section of road R and is minimized by minimizing the speed v along a curve X at the given legal speed limit v. Lis maintained. The accumulated longitudinal and lateral acceleration represent the discomfort of the driver and all passengers of vehicle 10 along road R.

[0028] The fourth subexpression ∫0sH(s)ds is an accumulated deviation from the recorded traffic flow speed v tf (s). Here, the deviation H(s) from the traffic flow speed is defined as H(s)=|v(s)−vtf(s)|

[0029] The traffic flow rate v tf (s) can be set by the driver or calculated, for example by the processor 50, which executes the instructions 52, by interpolating speeds from a list of nearby vehicles 10t detected by the radar distance sensor 32. For example, a list of nearby vehicles is given by (vi(sj),sj),j=1,2,...,M.

[0030] To interpolate the speed from a list of nearby vehicles, the list of nearby vehicles (v t (s j ), s j ) first, after the distance to the ego vehicle (e.g., the leading vehicle) at a point s j sorted. For example, if position s of the vehicle in question is 10 and the positions Sj-1 , s j the nearby vehicles are given 10t, where s j-1 ≤ s < s j , can the interpolated traffic flow velocity v tf (s) can be calculated as follows: vtf(s)=s−sj−1sj−sj−1vt(sj−1)+sj−ssj−sj−1vt(sj) Numerical techniques

[0031] Numerical techniques such as calculating variances can be used to find the minimum of equation 1 while satisfying the constraints of equations 2 and 3. To illustrate this, the above equations are discretized, and an optimal minimum is determined using a trellis graph (see...). Fig. 4) The arc length variable s is determined to discretized feasible places (s1, s2, ..., s). N The optimization determines speeds v i for i = 1, ..., N at these discrete points s i The discretized version of equations 1, 3, and 7 is given as ∑i=1N{C1Δsvi+C2|vivi−vi−1Δs|+C3[ki(vi2−vLi2)]+C4H(vi)} where ki=θi−θi−1Δs

[0032] The discretized versions of the restrictions of equations 2 and 3 are given as |vivi∼vj−1Δs|≤am and |vi−vLi|≤ε(or vi≤vLi+ε)

[0033] The index i = 1, 2, ..., N represents each position s under consideration. i , the velocity v i at every point s i and the curvature k i the road at every point s i The discretized velocities v i are a discretized version of the velocity profile v(s). Interpolation can be used to derive the velocity profile v(s) from the discretized velocities v i to determine. The number N is the number of digits s. i or the discretized velocities v i , which are to be used when determining the velocity profile v(s).

[0034] With reference to Fig. 3 and Fig. 5 are adjacent places s i by a segment spacing Δs = s i - s i-1 separated. The places s i approximate the road R. Tax rates θ(s) i ) and a curvature ki approximate the curve X, which was derived from the digital map 36. With reference to Fig. 4 are changes in the tax rate θ(s) i ) and the curvature k i for each position s i shown. Trellis graph

[0035] With reference to Fig. Section 5 describes the trellis graph G in further detail. The trellis graph G contains a matrix of vertically separated nodes. Each node represents a velocity at a specific location. The term "node" is used here to distinguish velocities used in optimization from other uses of the term velocity.

[0036] For clarification, represented with reference to Fig. 5. The subscript “i” of a node u represents a set of nodes in a column or a “disk”. The nodes u i where i = 1, ..., N represent discrete values ​​for velocities vi at those points i , which are used in optimization.

[0037] The method described here selects one of the nodes from a node disk u i as the optimal speed v i at the relevant point s i For clarification, a superscript is used to denote a subset of nodes in a node disk. When used here, the subset of nodes is called the "admissible" nodes because they satisfy the restrictions of equations 13 and 14. Other superscripts are used to show that the admissible nodes in other node disks are not necessarily the same. For example, the nodes denote uij (e.g. i = 2, j = 3, 4, 5; see also Fig. 5) permissible speeds at a point s i An optimal speed v i at one point s i is derived from the permissible nodes uij chosen.

[0038] The starting and ending speeds v0, v N at the start and end point s0, s N The trellis graph G is determined before the velocities v i at intermediate points s i (e.g., i = 1 ... N-1). The value of the current speed v0 can be determined as described above, e.g., by vehicle dynamics sensors 38 such as wheel pulse generators or by the GPS device 34. The value of the speed v N The endpoint can be any positive value that satisfies the restrictions of equations 13 and 14. For example, the velocity v can be N at the end point the speed limit v L at the end point s N be. Stretches and edges

[0039] With reference to Fig. Figures 5-9 represent possible velocity profiles v(s) along line segments P through the trellis graph G. Each line segment P contains a node u in each vertical disk, and nodes u in adjacent vertical disks are connected by edges E.

[0040] In particular, nodes in two adjacent vertical disks (e.g. ui−1j,uik ), which satisfy the conditions of equations 13 and 14, are coupled by an edge E (i.e., edges connect admissible nodes in adjacent vertical disks). In Fig. 5 - 8 are the conditions of equations 13 and 14 shown by dashed lines and the edges E are shown by solid lines.

[0041] In accordance with an exemplary procedure, edges E are defined between permissible upstream nodes u in a lower-numbered vertical disk (e.g., i-1) and permissible downstream nodes u in an adjacent higher-numbered vertical disk (e.g., i), satisfying the restrictions of equations 13 and 14. The step is then applied to the next adjacent higher-numbered vertical disks (e.g., i, i+1) using permissible nodes u to which an edge E is connected. Edges do not connect nodes in the same vertical disk.

[0042] In particular, with regard to Fig. 6 An exemplary procedure is the definition of edges E between the current velocity v0 at the starting point s0 and permissible downstream nodes u in the vertical disk of the point s1, where the conditions of equations 13 and 14 are satisfied. With reference to Fig. In step 7, the process is repeated for a feasible node u in the vertical disk at point s1, so that edges E are defined between the feasible node u in the vertical disk at point s1 and feasible downstream nodes u in the vertical disk at point s2. The exemplary procedure involves defining the edges E between other feasible nodes u in the vertical disk s1, which were connected to the current velocity v0 in the previous step, and feasible nodes u in the vertical disk at point s2. It is worth noting that the restriction of Equation 13 is applied relative to the upstream node u. All other edges E are defined similarly, leading to the connections described in Fig. 8 are shown.

[0043] The distances P between the current velocity v0 and the final velocity v NEach of the vertical slices contains a feasible node u. The nodes u of a line segment P are connected sequentially by edges E. Each line segment P represents a set of discretized velocities that can be chosen. To illustrate this, a collection π of line segments P through the trellis graph G from the initial velocity v0 to the final velocity v is shown. N defined as π(G;v0,vN)={(u0,u1k1,....uN−1kN−1,uN)|(uikI,....ui+1ki+1)∈E, uiki∈si}

[0044] Here is a route that starts at node u0 and ends at node u N ends, a set of nodes u over the positions s1, s2, ..., s N-1 of the trellis graph G one after the other. The notation (uiki,...ui+1ki+1)∈E is a condition that two nodes uik and ui+1ki+1 adjacent vertical disks (i and i+1) are coupled by an edge E for i = 0, ..., N-1. The notation uiki∈si is a condition that the node uiki to the vertical disk of point s i belongs. All admissible path lengths P contain a set of nodes u that satisfy these conditions. Cost of a route

[0045] To determine which path P should be used, the cost of each path P is calculated. The path P with the lowest cost is used to generate the velocity profile v(s). The cost of each path P is determined as a function of the cost of the nodes u and the cost of the edges E contained in each path P.

[0046] Each knot uik, the permissible speed vik (Velocity is used here because the value is used in the equation) in the i-th vertical disk, for i = 0, ..., N-1, has a cost value c, which is defined as c(uik)=C1[1vik]+C3[ki((vik)2)−vLi2]+C4H(vik) where H(vik)=|vik−vi(si)| and v t (s i ) is the interpolated traffic flow speed at a distance s i The cost value c of both the current speed v0 and the final speed v N is zero.

[0047] The cost value c of an edge E of nodes ui−1j to uik is defined as c(ui−1j,uik)=C2|vik(vik−vi−1j)Δs| and the index i = 1, ..., N-1. Each node in the vertical disk of the point s N-1 has an edge E that travels at the final velocity v N is coupled. The cost value c of the final velocity v Nis defined as zero.

[0048] By applying the cost values ​​c of the nodes u and the edges E, the cost value c of a path P is then defined by c(u0,u1k1,....uN−1kN−1,uN)=∑i=1N−1c(viki,vi+1ki+1)+c(viki)

[0049] Here, the set of nodes u contained in the line segment P is used to determine the cost value c. The minimum of equation 10 is the line segment P with the minimum cost value c. Dynamic programming

[0050] I(x) denotes the disk number of node x. The path P through the trellis graph G with the minimum cost c can be found using dynamic programming (DP) by recursively calculating d(x). d(x)={0x=u0min{d(y)+c(y,x)+c(x)}Otherwise which recursively determines the path with minimum cost from the starting node u0 to node x. Here, y is a set of nodes that satisfies the following conditions: l(y)=l(x)−1 and (y,x)∈E (i.e., there is an edge between the nodes y, x in the trellis graph G).

[0051] In summary, the dynamic programming method applied to the trellis graph is used to determine the velocity profile v(s). For example, recursive methods such as Dijkstra's shortest path algorithm can be used to find the path with minimum cost that corresponds to the set of velocities v. i in the set of places s i corresponds to a term that minimizes the foregoing expression and satisfies the constraints. With reference to Fig. 9 is the chosen distance P (set of velocities v) i at places s i ) and the resulting velocity profile v(s) is shown. Speed ​​profile

[0052] The velocity profile v(s) can be derived by discretizing the velocities v i in the set of places s i Interpolation is performed for i=1, ..., N-1. If the velocity profile v(s) with respect to the points s is given (e.g., the arc length s as a parameter), profiles for the planned vehicle position s(t), velocity v(t), and acceleration a(t) with respect to time can be determined. The vehicle position s(t) is determined by solving the following ordinary differential equation: dsdt=v(s)

[0053] The velocity v(t) and the acceleration a(t) are determined by using the time derivative of the vehicle position s(t) and the velocity v(t), respectively. v(t)=ds(t)dt and α(t)=dv(t)dt

[0054] The calculated vehicle position s(t), velocity v(t) and acceleration a(t) with respect to time can be easily used by the vehicle longitudinal controller.

[0055] According to an exemplary embodiment, application 62 for adaptive speed control contains instructions executable by a computer for generating a command as a function of the speed v. ta vehicle 10t that is closest to the distance measured by the radar distance sensor 32. The vehicle 10t that is closest to the distance is one of the nearby vehicles 10t, is closest to vehicle 10, and is closer than a distance threshold dr (e.g., one hundred and twenty meters). In particular, the vehicle 10 is controlled by the application 62 for adaptive cruise control when a vehicle 10t that is closest to the distance is detected by the radar distance sensor 32 and the distance (the gap between the CIPV (nearest vehicle on the distance) and the ego vehicle) is less than the distance threshold dr.

[0056] According to an exemplary embodiment, the selection application 64 contains computer-executable instructions for selecting a speed command. In particular, the selection application 64 selects between the speed command of the speed optimization application 60 and the speed command of the adaptive cruise control application 62 according to a method 100. The selected speed command is sent to the throttle / brake controller 22.

[0057] With reference to Fig.Section 10 describes the procedure 100 for switching between a command generated by the speed optimization application 60 and a command generated by the adaptive cruise control application 62. Starting from a starting point 101, the selection application 64 determines, according to a first step 102, whether a vehicle closest to the track exists. If so, the speed controller 30 selects, according to a second step 104, the command generated by the adaptive cruise control application 62. Otherwise, the speed controller 30 selects, according to a third step 106, the command generated by the speed optimization application 60. Example scenarios

[0058] On a straight road R and without other traffic, the speed optimization application 60 generates a constant speed profile v(s) equal to the specified speed limit v.L or a speed set by the driver. On a straight road with other traffic, but without a vehicle v t , which is closest on the route, the speed optimization application 60 generates a speed profile v(s) that corresponds to the traffic flow speed v tf It is similar. When entering the curve of a road R, the speed optimization application 60 generates a continuous speed profile v(s) to decelerate at the curve X. When exiting the curve of a road R, the speed optimization application 60 generates a continuous speed profile v(s) to accelerate after passing through the curve X.

[0059] The embodiments described above are merely illustrated implementations disclosed for a clear understanding of the principles. Variations, modifications, and combinations of the embodiments described above may be carried out without departing from the scope of the claims. All such variations, modifications, and combinations are contained herein by the scope of this disclosure and the following claims.

Claims

[1] Cruise control system (20) comprising: a position determination sensor (34) configured to obtain a set of locations (s) along a road (R); a distance sensor (32) configured to measure a distance (dt) to one or more nearby vehicles (10t); a speed sensor configured to detect a speed (v t ) of one or more nearby vehicles (10t); a processor (50); and a memory (52) having computer-executable instructions stored therein, comprising: a speed optimization application (60) which, when executed by the processor (50), causes the processor (50) to determine a speed profile (v(s)), the speed profile (v(s)) comprising a set of speeds (v) corresponding to the set of locations (s), the speed profile (v(s)) being determined as the minimum of: a first subexpression representing a time to travel through the set of places (s), a second subexpression representing a longitudinal acceleration of the vehicle (10) at the set of locations (s); a third subexpression representing a lateral acceleration of the vehicle (10) at the set of locations (s); and a fourth subexpression representing an accumulated deviation from a traffic flow speed; wherein the cruise control system (20) further comprises a selection application (64) comprising computer-executable instructions which, when executed by the processor (50), cause the processor (50): indicates the presence of a nearby vehicle (10t) that is closest on the route if at least one distance (dt) to a nearby vehicle (10t) measured by the distance sensor (32) is less than a distance threshold (dr), and selects a speed command of an adaptive cruise control application (62) for a throttle / brake controller (22); and otherwise indicates the absence of a nearby vehicle (10t) closest on the route and selects a speed command of the speed optimization application (60) for the throttle / brake controller (22). [2] A speed control system (20) according to claim 1, wherein the first sub-expression is a function of the inverse of the speed (v). [3] A speed control system (20) according to claim 1, wherein the first partial expression ∫0S1v(s)ds where s is a location, S is an arc length, and v(s) is the velocity profile. [4] A speed control system (20) according to claim 1, wherein the second sub-expression is a function of the derivative of the speed (v) with respect to the location. [5] A speed control system (20) according to claim 1, wherein the second partial expression ∫0S|v(s)dv(s)ds|ds where s is the location, S is the arc length and v(s) is the velocity profile. [6] A speed control system (20) according to claim 1, wherein the third sub-expression is a function of the derivative of a heading angle (θ(s)) with respect to the location (s) and the difference between the square of the speed (v) and the square of a speed limit (v L ) is. [7] A speed control system (20) according to claim 1, wherein the third subexpression ∫0Sk(s)(v(s)2−vL(s)2)ds where s is a location, S is an arc length and v(s) is the velocity profile, v L (s) is a speed limit at the point and k(s) is a curvature given by k(s)=|dθ(s)ds| where θ(s) is a course target angle. [8] The cruise control system (20) of claim 1, wherein the fourth sub-term is a function of the difference between a traffic flow speed and the speed of the vehicle (10). [9] A speed control system (20) according to claim 1, wherein the fourth subexpression ∫0SH(s)ds where H(s) is given by H(s)=|v(s)−v,(s)| where v(s) is a velocity profile and v t (s) is a traffic flow speed.

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