WO2018188177A1 - 一种基于无人驾驶的路径规划的方法、装置及系统 - Google Patents
一种基于无人驾驶的路径规划的方法、装置及系统 Download PDFInfo
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- WO2018188177A1 WO2018188177A1 PCT/CN2017/086327 CN2017086327W WO2018188177A1 WO 2018188177 A1 WO2018188177 A1 WO 2018188177A1 CN 2017086327 W CN2017086327 W CN 2017086327W WO 2018188177 A1 WO2018188177 A1 WO 2018188177A1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3469—Fuel consumption; Energy use; Emission aspects
Definitions
- the invention belongs to the field of driverless technology, and in particular relates to a method, device and system based on unmanned path planning.
- the traffic signal conversion cycle in the traffic control department has a public timetable to provide specific data queries; this brings real-time calculations for navigation.
- the traffic light conversion time to optimize the vehicle navigation control tuning speed scheme provides a reliable data base.
- one of the objects of the present invention is to provide a method based on unmanned path planning that achieves accurate estimation of travel time and optimizes driving experience.
- a second object of the present invention is an apparatus based on unmanned path planning that achieves accurate estimation of travel time and optimizes the driving experience.
- a third object of the present invention is a system based on unmanned path planning that achieves accurate estimation of travel time and optimizes the driving experience.
- a method based on driverless path planning comprising the following steps:
- S1 calculating a starting parameter and a braking parameter according to the acquired vehicle parameters, and correspondingly controlling the vehicle according to the starting parameter and the braking parameter, the vehicle parameter including a starting acceleration and a braking acceleration, the starting parameter including a starting distance and a starting time, the braking Parameters include braking distance and braking time;
- S2 acquiring navigation road information between the starting point and the destination, where the navigation road information includes a navigation path, a traffic light signal, and road condition information;
- S3 acquiring a current driving state of the vehicle, where the current driving state of the vehicle includes current location information of the vehicle and a traveling speed of the vehicle;
- S6 Determine whether the driving speed of the vehicle is adapted to the tuning speed, and if not, adjust the vehicle speed to the tuning speed.
- the step S2 specifically includes the following sub-steps:
- S22 Identify all traffic light signals on the navigation path, where the traffic light signal includes a traffic light position, a traffic light state, and a traffic light conversion rule;
- the navigation path is divided into a plurality of road segments according to the position of the traffic light to obtain a navigation road segment;
- S24 Obtain road condition information of each navigation road segment, where the road condition information includes average vehicle speed and speed limit information.
- the step S5 specifically includes the following sub-steps:
- S51 Calculate a time required for each vehicle speed in the floating vehicle speed group to pass each navigation section according to the floating vehicle speed group, the current position of the vehicle, and the road condition information;
- S52 Calculate the sum of the waiting times of each navigation section, the traffic light, and the traffic light in each speed of the floating speed group;
- S53 Calculate the corresponding vehicle speed in the floating vehicle speed group that has the shortest total time to reach the destination by the tuning calculation formula, that is, adjust the vehicle speed.
- the formula for calculating the tempered vehicle speed used in step S53 is:
- t vi represents the time required to advance at speed v, to reach the ith traffic light
- t wi represents the waiting time required at the ith traffic light
- t ci represents the time required to cross the ith traffic light intersection
- f(v ) represents a function that finds the speed v that minimizes the total accumulated time.
- a device based on unmanned path planning comprising the following modules:
- the parameter calculation module is configured to calculate a starting parameter and a braking parameter according to the acquired vehicle parameters, and correspondingly control the vehicle according to the starting parameter and the braking parameter, the vehicle parameter includes a starting acceleration and a braking acceleration, and the starting parameter includes a starting distance and a starting point.
- Time, the braking parameters include braking distance and braking time;
- a navigation information acquiring module configured to acquire navigation road information between a starting point and a destination, where the navigation road information includes a navigation path, a traffic light signal, and road condition information;
- a vehicle state acquisition module configured to acquire a current driving state of the vehicle, where the current driving state of the vehicle includes current location information of the vehicle and a traveling speed of the vehicle;
- the floating vehicle speed calculation module is configured to obtain a floating vehicle speed group according to the current driving state of the vehicle and the navigation road information;
- Tuning vehicle speed module used to obtain a floating vehicle speed group according to the current driving state of the vehicle and the navigation road information;
- Vehicle speed control module used to determine whether the vehicle's driving speed and the tuned vehicle speed are suitable. If not, adjust the vehicle speed to the tuned vehicle speed.
- the navigation information acquiring module specifically includes the following submodules:
- the navigation path obtaining module is configured to acquire a navigation path between the starting point and the destination;
- the traffic light recognition module is configured to identify all traffic light signals on the navigation path, where the traffic light signal includes a traffic light position, a traffic light state, and a traffic light conversion rule;
- the navigation segment segmentation module is configured to divide the navigation path into a plurality of segments according to the position of the traffic light to obtain a navigation segment;
- the road information acquiring module is configured to acquire road condition information of each navigation road segment, where the road condition information includes average vehicle speed and speed limit information.
- the tuning vehicle speed module specifically includes the following submodules:
- the navigation section time module is configured to calculate the time required for each vehicle speed in the floating speed group to pass each navigation section according to the floating speed group, the current position of the vehicle, and the road condition information;
- Time calculation module used to calculate the sum of time waiting through each navigation section, traffic lights, and traffic lights in each speed of the floating speed group;
- Tuning speed calculation module used to calculate the corresponding minimum speed of the total time to reach the destination in the floating speed group by tuning calculation formula, which is to adjust the speed.
- the formula for calculating the tempered vehicle speed used in the tuning vehicle speed calculation module is:
- t vi represents the time required to advance at speed v, to reach the ith traffic light
- t wi represents the waiting time required at the ith traffic light
- t ci represents the time required to cross the ith traffic light intersection
- f(v ) represents a function that finds the speed v that minimizes the total accumulated time.
- a system based on driverless path planning comprising an actuator for performing a method based on driverless path planning as described in any of the above.
- the method of the invention based on driverless path planning can help achieve in no one In the case of driving, by fine-tuning the speed while driving, avoid waiting time for the traffic lights to be too long, to achieve the purpose of optimizing the driving experience, green low-carbon travel.
- FIG. 1 is a flow chart of a method for unmanned path planning according to the present invention
- FIG. 2 is a structural diagram of an apparatus for unmanned route planning according to the present invention.
- the prior art path planning algorithm does not consider the processing of the intersection, lacks the traffic signal conversion cycle combined with the intersections, the waiting time through the intersection, the driving speed of the owner, the traffic flow, the comprehensive calculation planning route, the arrival time, and also There is no real-time reminder to the owner or to remind the vehicle to drive at the optimal speed to avoid waiting or reducing the waiting time to navigate through the intersection with the signal light.
- the unmanned driving technology is becoming more and more mature, but there is no relevant content for publicizing the design method of the vehicle speed according to the navigation path of the traffic light.
- the present invention provides a method for unmanned path planning, including the following steps:
- S1 calculating a starting parameter and a braking parameter according to the acquired vehicle parameters, and correspondingly controlling the vehicle according to the starting parameter and the braking parameter, the vehicle parameter including a starting acceleration and a braking acceleration, the starting parameter including a starting distance and a starting time, the braking Parameters include braking distance and braking time;
- the starting distance and the starting time are correspondingly set to achieve the calculated optimized vehicle speed, and the safe braking distance and the braking time are calculated to control the vehicle to stop at each intersection that needs parking waiting;
- Each type of car has a corresponding parameter configuration at the factory. This parameter is pre-set in the system and can be automatically matched when the corresponding model is selected.
- step S2 Acquire navigation road information between the starting point and the destination, the navigation road information includes a navigation path, a traffic light signal, and road condition information; the step S1 specifically includes the following sub-steps:
- S22 Identify all traffic light signals on the navigation path, where the traffic light signal includes a traffic light position, a traffic light state, and a traffic light conversion rule; the traffic light signal conversion cycle has a public timetable provided by the traffic control department to provide a specific data query.
- the related data is stored in a data server for data support, and the server of the present invention Connected to the Internet to obtain real-time dynamic traffic control and traffic control data for the updated car network or traffic control department;
- S24 Obtain road condition information of each navigation section, where the road condition information includes average vehicle speed and speed limit information; according to the average vehicle speed and the speed limit information, the reference calculation information is used as the tuning speed, for example, the range of the adjusted vehicle speed cannot be high.
- the speed limit of the road on the corresponding road section is used as the tuning speed, for example, the range of the adjusted vehicle speed cannot be high.
- S3 acquiring a current driving state of the vehicle, the current driving state of the vehicle includes current location information of the vehicle and a traveling speed of the vehicle; positioning the current location of the vehicle by using GPS positioning and other auxiliary positioning functions, and detecting the vehicle in unit time The travel distance within the vehicle is used to measure the current travel speed of the vehicle.
- S4 obtaining a floating vehicle speed group according to the current driving state of the vehicle and the navigation road information; the floating vehicle speed group floating up and down a range based on the current vehicle speed to obtain a set of floating vehicle speed; the floating vehicle speed group may be a continuous threshold range, It can be that all speed values form an arithmetic progression and then form a floating speed group.
- the floating vehicle speed group used in the method of the present invention is set based on the current actual vehicle speed of the vehicle, instead of being determined by the system to make the user decide, making the implementation more humanized, more convenient, and easier to promote;
- step S5 According to each speed in the floating speed group, the time required for the full driving time is obtained, and the vehicle speed corresponding to the shortest running time is used as the tuning speed; the floating speed group is used to calculate the total time to reach the destination, and the comparison result is obtained.
- the shortest overall arrival time, the driving speed corresponding to the shortest overall arrival time is the result data of the tuned vehicle speed; the overall arrival time is when the driving time of each navigational section between the traffic lights and the traffic light of each intersection is switched.
- the sum of the steps; the step S4 specifically includes the following sub-steps:
- S51 Calculate the time required for each vehicle speed in the floating vehicle speed group to pass each navigation section according to the floating vehicle speed group, the current position of the vehicle, and the road condition information; respectively calculate the time required for each vehicle speed in the floating vehicle speed group to pass each navigation segment path .
- the time consider the effect of the time required to stop the acceleration at each intersection because of the different speed of the vehicle, the average speed of the road vehicle, the speed limit data of the road, etc., on the overall transit time;
- S52 Calculate the sum of the waiting times of each navigation section, the traffic light, and the traffic light in each speed of the floating speed group;
- step S53 Calculate the corresponding vehicle speed with the shortest total time to reach the destination in the floating vehicle speed group by using the tuning calculation formula, that is, the tuned vehicle speed; the formula for calculating the tempered vehicle speed used in step S45 is:
- t vi represents the time required to advance at the speed v and reach the ith traffic light, calculated by dividing the distance by the speed, t wi indicating the waiting time required at the ith traffic light, and calculating the corresponding traffic light state when the traffic light arrives at the traffic light The waiting time is determined; t ci represents the time required to traverse the i-th traffic light intersection; f(v) represents a function of the speed v at which the cumulative total time is minimized.
- S6 It is judged whether the running speed of the vehicle is consistent with the tuning speed, and if not, the vehicle speed is adjusted to the tuning speed. Or the owner can select the navigation path according to the actual situation, and select the path with the least time to reach the destination. When there is congestion on the navigation path or some roads are convenient and cannot be displayed through the map, the user can independently select the corresponding The path is used to plan the driving path and recalculate the tuning speed on the navigation path.
- the method of unmanned path planning can be made more widely used by increasing user settings.
- the method for planning the vehicle speed of the navigation path of the present invention can also be applied to the situation of the traffic light when the next intersection is reached, by calculating the distance from the current vehicle speed to the next intersection, and obtaining the tuning speed according to the state of the traffic signal, so that At the next intersection, the status of the traffic light is green, so that it can pass through the intersection.
- the driving path can also be anywhere before navigating to the traffic light intersection.
- the road map and the GPS positioning and other auxiliary positioning functions of the existing navigation system are used to calculate the time required to wait for the signal light when passing each intersection according to the traffic signal conversion period data and the current driving speed. Integrate the traffic conditions of each section to calculate the travel time, plan the route navigation and adjust the optimal speed to help the owner to more accurately estimate the travel time, select the navigation line, and control the vehicle to adjust the speed. By fine-tuning the speed when driving, avoiding the waiting time of the traffic lights is too long, to achieve an optimized driving experience, the purpose of green low-carbon travel, and even the state of green light passing through various intersections from the starting point to the end point.
- the present invention provides an apparatus for unmanned path planning, including the following modules:
- a navigation information acquiring module configured to acquire navigation road information between a starting point and a destination, where the navigation road information includes a navigation path, a traffic light signal, and road condition information;
- the navigation information acquisition module specifically includes the following sub-modules:
- the navigation path obtaining module is configured to acquire a navigation path between the starting point and the destination;
- the traffic light recognition module is configured to identify all traffic light signals on the navigation path, where the traffic light signal includes a traffic light position, a traffic light state, and a traffic light conversion rule;
- the navigation segment segmentation module is configured to divide the navigation path into a plurality of segments according to the position of the traffic light to obtain a navigation segment;
- the road information acquiring module is configured to acquire road condition information of each navigation road segment, where the road condition information includes average vehicle speed and speed limit information;
- a vehicle state acquisition module configured to acquire a current driving state of the vehicle, where the current driving state of the vehicle includes current location information of the vehicle and a traveling speed of the vehicle;
- the floating vehicle speed calculation module is configured to obtain a floating vehicle speed group according to the current driving state of the vehicle and the navigation road information;
- the tuned vehicle speed module is configured to obtain a floating vehicle speed group according to the current driving state of the vehicle and the navigation road information; the tuned vehicle speed module specifically includes the following sub-modules:
- the navigation section time module is configured to calculate the time required for each vehicle speed in the floating speed group to pass each navigation section according to the floating speed group, the current position of the vehicle, and the road condition information;
- Time calculation module used to calculate the sum of time waiting through each navigation section, traffic lights, and traffic lights in each speed of the floating speed group;
- Tuning speed calculation module used to calculate the corresponding minimum speed of the total time to reach the destination in the floating speed group by the tuning calculation formula, which is the tuning speed; the formula of the tuning speed adopted in the tuning speed calculation module For:
- t vi represents the time required to advance at speed v, to reach the ith traffic light
- t wi represents the waiting time required at the ith traffic light
- t ci represents the time required to cross the ith traffic light intersection
- f(v ) a function that finds the velocity v that minimizes the total accumulated time
- Vehicle speed control module used to determine whether the vehicle's driving speed is consistent with the tuning speed. If not, adjust the vehicle speed to the tuning speed.
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Abstract
一种基于无人驾驶的路径规划的方法、装置及系统，方法包括以下步骤：S1：获取车辆参数信息；S2：获取起点至目的地之间的导航道路信息，导航道路信息包括导航路径、红绿灯信号和道路状况信息；S3：获取车辆当前的行驶状态，车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；S4：根据车辆的当前行驶状态和导航道路信息得到浮动车速组；S5：根据浮动车速组中的各车速得到行驶完全程所需时间，将行驶时间最短所对应的车速作为调优车速；S6：判断车辆的行驶速度与调优车速是否适应，如果否，调整车速至调优车速。本方法通过微调车速，避免红绿灯等待时间长，达到优化驾驶体验，绿色低碳出行的目的。
Description
本发明属于无人驾驶技术领域，尤其涉及一种基于无人驾驶的路径规划的方法、装置及系统。
随着无人驾驶技术的不断发展和深入研究，人们驾车出行，使用无人驾驶技术提高用车出行体验的综合需求也逐步成为可能；车辆在行驶过程中，难免会遇到有信号灯的交叉口，在交叉口左转、右转、直行时的信号灯转换等待时间。如何通过避免频繁刹车、起步加速和长时间停车来减少碳排放量，减少能源消耗、达到节能减排的目的也成为我们需要考虑的重要技术环节。现有技术中，没有通过控制车速快慢达到效减少或避免红绿灯等候的时间的车辆导航及控制方式。
随着车联网技术的不断发展，应用在红绿灯信号变化数据获取方式也越来越多，同时，红绿灯信号转换周期在交管部门都有公开时刻表提供具体数据查询；这都为导航中引入实时计算红绿灯转换时间来优化车辆导航控制调优车速方案提供可靠的数据基础。
当前在无人驾驶领域也还没有对于整个导航路径进行规划时采用车速调优来规避红绿灯等候时间过长或避在红绿灯路口停车等待的技术方案。
发明内容
为了克服现有技术的不足，本发明的目的之一在于提供一种基于无人驾驶的路径规划的方法，其达到精确估计行驶时间，优化驾驶体验。
本发明的目的之二在于基于无人驾驶的路径规划的装置，其达到精确估计行驶时间，优化驾驶体验。
本发明的目的之三在于基于无人驾驶的路径规划的系统，其达到精确估计行驶时间，优化驾驶体验。
本发明的目的之一采用以下技术方案实现：
一种基于无人驾驶的路径规划的方法，包括以下步骤：
S1：根据获取到的车辆参数计算起步参数和刹车参数，并根据起步参数和刹车参数对车辆进行相应控制，该车辆参数包括起步加速度和刹车加速度，该起步参数包括起步距离和起步时间，该刹车参数包括刹车距离和刹车时间；
S2：获取起点至目的地之间的导航道路信息，该导航道路信息包括导航路径、红绿灯信号和道路状况信息；
S3：获取车辆当前的行驶状态，该车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；
S4：根据车辆的当前行驶状态和导航道路信息得到浮动车速组；
S5：根据浮动车速组中的各车速得到行驶完全程所需时间，将行
驶时间最短所对应的车速作为调优车速；
S6：判断车辆的行驶速度与调优车速是否适应，如果否，则调整车速至调优车速。
优选的，所述步骤S2具体包括以下子步骤：
S21：获取起点与目的地之间的导航路径；
S22：识别导航路径上所有的红绿灯信号，所述红绿灯信号包括红绿灯位置、红绿灯状态和红绿灯转换规则；
S23：根据红绿灯位置将导航路径切分成多个路段以得到导航路段；
S24：获取各个导航路段的道路状况信息，该道路状况信息包括平均车速和限速信息。
优选的，所述步骤S5具体包括以下子步骤：
S51：根据浮动车速组、车辆的当前位置和道路状况信息以计算浮动车速组中各车速通过各导航路段所需要的时间；
S52：以浮动车速组中各车速计算通过各导航路段、红绿灯以及红绿灯等待的时间之和；
S53：通过调优计算公式计算得到浮动车速组中到达目的地的总体时间最短的对应车速，即为调优车速。
优选的，在步骤S53中采用的调优车速计算公式为：
其中，tvi表示以速度v前进，到达第i个红绿灯所需时间，twi
表示在第i个红绿灯所需的等待时间，tci表示横穿第i个红绿灯路口所需时间；f(v)表示求得使累计总时间最短的速度v的函数。
本发明的目的之二采用以下技术方案实现：
一种基于无人驾驶的路径规划的装置，包括以下模块：
参数计算模块：用于根据获取到的车辆参数计算起步参数和刹车参数，并根据起步参数和刹车参数对车辆进行相应控制，该车辆参数包括起步加速度和刹车加速度，该起步参数包括起步距离和起步时间，该刹车参数包括刹车距离和刹车时间；
导航信息获取模块：用于获取起点至目的地之间的导航道路信息，该导航道路信息包括导航路径、红绿灯信号和道路状况信息；
车辆状态获取模块：用于获取车辆当前的行驶状态，该车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；
浮动车速计算模块：用于根据车辆的当前行驶状态和导航道路信息得到浮动车速组；
调优车速模块：用于根据车辆的当前行驶状态和导航道路信息得到浮动车速组；
车速控制模块：用于判断车辆的行驶速度与调优车速是否适应，如果否，则调整车速至调优车速。
优选的，所述导航信息获取模块具体包括以下子模块：
导航路径获取模块：用于获取起点与目的地之间的导航路径；
红绿灯识别模块：用于识别导航路径上所有的红绿灯信号，所述红绿灯信号包括红绿灯位置、红绿灯状态和红绿灯转换规则；
导航路段切分模块：用于根据红绿灯位置将导航路径切分成多个路段以得到导航路段；
道路信息获取模块：用于获取各个导航路段的道路状况信息，该道路状况信息包括平均车速和限速信息。
优选的，所述调优车速模块具体包括以下子模块：
导航路段时间模块：用于根据浮动车速组、车辆的当前位置和道路状况信息以计算浮动车速组中各车速通过各导航路段所需要的时间；
时间计算模块：用于以浮动车速组中各车速计算通过各导航路段、红绿灯以及红绿灯等待的时间之和；
调优车速计算模块：用于通过调优计算公式计算得到浮动车速组中到达目的地的总体时间最短的对应车速，即为调优车速。
优选的，在调优车速计算模块中采用的调优车速计算公式为：
其中，tvi表示以速度v前进，到达第i个红绿灯所需时间，twi表示在第i个红绿灯所需的等待时间，tci表示横穿第i个红绿灯路口所需时间；f(v)表示求得使累计总时间最短的速度v的函数。
本发明的目的之三采用以下技术方案实现：
一种基于无人驾驶的路径规划的系统，包括执行器，所述执行器用于执行如上述任意一项所述的基于无人驾驶的路径规划的方法。
相比现有技术，本发明的有益效果在于：
本发明的基于无人驾驶的路径规划的方法，能够帮助实现在无人
驾驶的情况下，通过在行车时通过微调车速，避免红绿灯等待时间过长，达到优化行车体验，绿色低碳出行的目的。
图1为本发明的基于无人驾驶的路径规划的方法的流程图；
图2为本发明的基于无人驾驶的路径规划的装置的结构图。
下面，结合附图以及具体实施方式，对本发明做进一步描述：
现有技术路径规划算法没有考虑交叉口的处理，缺乏结合各路口的交通信号灯转换周期、通过路口需要等候的时间、车主的驾驶速度、车流情况，综合计算规划路径、到达目的地时间，同时也没有实时提示车主或提醒车辆以最优车速行驶避免等候或减少等候时间来通过有信号灯的交叉路口的导航方式。并且目前无人驾驶技术也越来越成熟，但是针对于根据红绿灯的导航路径的车速调优设计方式并没有相关的内容进行公开。
如图1所示，本发明提供了一种基于无人驾驶的路径规划的方法，包括以下步骤：
S1：根据获取到的车辆参数计算起步参数和刹车参数，并根据起步参数和刹车参数对车辆进行相应控制，该车辆参数包括起步加速度和刹车加速度，该起步参数包括起步距离和起步时间，该刹车参数包括刹车距离和刹车时间；
在本发明中，需要获取车辆的相关参数，比如需要获得每种型号的车的起步加速度和刹车加速度，也即是每种型号的车所对应的百米加速度和刹车时车辆相对应的加速度，从而来对应设置起步距离和起步时间以达到计算的优化车速，计算安全刹车距离和刹车时间，来控制车辆在每个需要停车等待的路口停车；
每种型号的车在出厂时都有对应的参数配置提供，这个参数预先在系统内汇总设置好，在进行使用时选择对应车型即可自动匹配；
根据车辆参数计算好开始刹车的距离和刹车时间，发送给车辆控制系统，控制车辆减速停车，起步加速也是一样的，根据车辆的参数配置，计算一个起步距离和达到优化车速的时间，然后控制车辆起步；每种型号的车辆对应的参数不同，根据匀速控制车辆起步，停车和直线行驶能够控制车辆最省油的行车经验，得出上述控制车辆加减速的方法，并且起步距离和停车距离在实际应用的时候应该为对应车速的两倍；
S2：获取起点至目的地之间的导航道路信息，该导航道路信息包括导航路径、红绿灯信号和道路状况信息；所述步骤S1具体包括以下子步骤：
S21：获取起点与目的地之间的导航路径；
S22：识别导航路径上所有的红绿灯信号，所述红绿灯信号包括红绿灯位置、红绿灯状态和红绿灯转换规则；红绿灯信号转换周期在交管部门都有公开的时刻表提供具体的数据查询，在本发明中，将相关的数据都存储于一数据服务器中来进行数据支持，且本发明服务器
与互联网连接，能够实时获取更新车联网或交管部门的动态交通管制和信号灯控制数据；
S23：根据红绿灯位置将导航路径切分成多个路段以得到导航路段，并保存各路段的开始和结束位置的信息；
S24：获取各个导航路段的道路状况信息，该道路状况信息包括平均车速和限速信息；根据平均车速和限速信息都是作为调优车速的参考计算信息，比如调优车速的范围不能够高于相应路段的道路限速。
S3：获取车辆当前的行驶状态，该车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；通过GPS定位以及其他辅助的定位功能来定位车辆的当前位置，并通过检测车辆在单位时间内的行驶距离来测量得到车辆的当前行驶速度。
S4：根据车辆的当前行驶状态和导航道路信息得到浮动车速组；该浮动车速组以当前车速为基础上下浮动一个范围，得到一组浮动车速；该浮动车速组可以是一个连续的阈值范围，也可以是所有的速度值形成一个等差数列，然后组成浮动车速组。本发明的方法中采用的浮动车速组是基于车辆当前的实际车速来进行设定的，而不是通过系统来帮用户决定，使得实现起来更人性化，更方便，也更易于推广；
S5：根据浮动车速组中的各车速得到行驶完全程所需时间，将行驶时间最短所对应的车速作为调优车速；用浮动车速组来分别计算到达目的地的总体时间，通过比较结果得出最短总体到达时间，对应最短总体到达时间的行驶车速则为调优车速的结果数据；总体到达时间为通过红绿灯之间各导航路段行驶时间和各路口红绿灯切换等待时
间的总和；所述步骤S4具体包括以下子步骤：
S51：根据浮动车速组、车辆的当前位置和道路状况信息以计算浮动车速组中各车速通过各导航路段所需要的时间；分别计算浮动车速组中各车速通过各个导航分段路径所需要的时间。在计算时间时，同时考虑在各个路口停车起步加速因为车速不同所需要的时间、道路车辆平均车速、道路限速数据等情况对整体通过时间的影响；
S52：以浮动车速组中各车速计算通过各导航路段、红绿灯以及红绿灯等待的时间之和；
S53：通过调优计算公式计算得到浮动车速组中到达目的地的总体时间最短的对应车速，即为调优车速；在步骤S45中采用的调优车速计算公式为：
其中，tvi表示以速度v前进，到达第i个红绿灯所需时间，通过距离除以速度计算出来，twi表示在第i个红绿灯所需的等待时间，通过计算到达红绿灯时对应的红绿灯状态确定需等待时间；tci表示横穿第i个红绿灯路口所需时间；f(v)表示求得使累计总时间最短的速度v的函数。
S6：判断车辆的行驶速度与调优车速是否一致，如果否，则调整车速至调优车速。或者是车主可以根据实际情况来对导航路径进行选择，选择到达目的地用时最少的路径，当导航路径上有拥堵的情况或者有些道路比较方便而没能通过地图显示的，用户可以自主通过选择相应路径来进行行车路径规划，并重新计算导航路径上的调优车速，
通过增加用户设置可以使得该无人驾驶的路径规划的方法使用范围更加的广泛。
本发明的导航路径规划车速的方法还可以适用于到达下一个路口时，该交通灯的情况，通过计算当前车速到下一个路口的距离，并根据红绿灯信号的状态来得到调优速度，使得当到下一个路口的时候红绿灯的状态为绿灯，从而可以顺利通过路口，该行车路径还可以是导航至红绿灯路口前的任意位置。当更多的车辆采用本发明基于本发明根据导航路径规划车速的方法的系统的时候，可以通过获取其他车辆的信息，比如将车辆的行驶速度作为本发明方法的规划车速的参考信息，能够更准确估计时间。
本发明的根据导航路径规划车速的方法，依托现有导航系统的道路地图和GPS定位及其他辅助定位功能，根据红绿灯信号转换周期数据、结合当前行车速度计算通过各个路口时需要等候信号灯的时间，综合各路段车流情况等因素进行计算行车时间、规划线路导航和调整最优车速，帮助车主在出行时更精确估计行驶时间、选择导航线路、控制车辆以调优车速行驶。并在行车时通过微调车速，避免红绿灯等待时间过长，达到优化驾驶体验，绿色低碳出行的目的，甚至可以达到从起点到终点通过各个路口一路绿灯的状态。
如图2所示，本发明提供了一种基于无人驾驶的路径规划的装置，包括以下模块：
导航信息获取模块：用于获取起点至目的地之间的导航道路信息，该导航道路信息包括导航路径、红绿灯信号和道路状况信息；所述导
航信息获取模块具体包括以下子模块：
导航路径获取模块：用于获取起点与目的地之间的导航路径；
红绿灯识别模块：用于识别导航路径上所有的红绿灯信号，所述红绿灯信号包括红绿灯位置、红绿灯状态和红绿灯转换规则；
导航路段切分模块：用于根据红绿灯位置将导航路径切分成多个路段以得到导航路段；
道路信息获取模块：用于获取各个导航路段的道路状况信息，该道路状况信息包括平均车速和限速信息；
车辆状态获取模块：用于获取车辆当前的行驶状态，该车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；
浮动车速计算模块：用于根据车辆的当前行驶状态和导航道路信息得到浮动车速组；
调优车速模块：用于根据车辆的当前行驶状态和导航道路信息得到浮动车速组；所述调优车速模块具体包括以下子模块：
导航路段时间模块：用于根据浮动车速组、车辆的当前位置和道路状况信息以计算浮动车速组中各车速通过各导航路段所需要的时间；
时间计算模块：用于以浮动车速组中各车速计算通过各导航路段、红绿灯以及红绿灯等待的时间之和；
调优车速计算模块：用于通过调优计算公式计算得到浮动车速组中到达目的地的总体时间最短的对应车速，即为调优车速；在调优车速计算模块中采用的调优车速计算公式为：
其中，tvi表示以速度v前进，到达第i个红绿灯所需时间，twi表示在第i个红绿灯所需的等待时间，tci表示横穿第i个红绿灯路口所需时间；f(v)表示求得使累计总时间最短的速度v的函数；
车速控制模块：用于判断车辆的行驶速度与调优车速是否一致，如果否，则调整车速至调优车速。
对本领域的技术人员来说，可根据以上描述的技术方案以及构思，做出其它各种相应的改变以及形变，而所有的这些改变以及形变都应该属于本发明权利要求的保护范围之内。
Claims (9)
- 一种基于无人驾驶的路径规划的方法，其特征在于，包括以下步骤：S1：根据获取到的车辆参数计算起步参数和刹车参数，并根据起步参数和刹车参数对车辆进行相应控制，该车辆参数包括起步加速度和刹车加速度，该起步参数包括起步距离和起步时间，该刹车参数包括刹车距离和刹车时间；S2：获取起点至目的地之间的导航道路信息，该导航道路信息包括导航路径、红绿灯信号和道路状况信息；S3：获取车辆当前的行驶状态，该车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；S4：根据车辆的当前行驶状态和导航道路信息得到浮动车速组；S5：根据浮动车速组中的各车速得到行驶完全程所需时间，将行驶时间最短所对应的车速作为调优车速；S6：判断车辆的行驶速度与调优车速是否适应，如果否，则调整车速至调优车速。
- 如权利要求1所述的基于无人驾驶的路径规划的方法，其特征在于，所述步骤S2具体包括以下子步骤：S21：获取起点与目的地之间的导航路径；S22：识别导航路径上所有的红绿灯信号，所述红绿灯信号包括红绿灯位置、红绿灯状态和红绿灯转换规则；S23：根据红绿灯位置将导航路径切分成多个路段以得到导航路段；S24：获取各个导航路段的道路状况信息，该道路状况信息包括平均车速和限速信息。
- 如权利要求1所述的基于无人驾驶的路径规划的方法，其特征在于，所述步骤S5具体包括以下子步骤：S51：根据浮动车速组、车辆的当前位置和道路状况信息以计算浮动车速组中各车速通过各导航路段所需要的时间；S52：以浮动车速组中各车速计算通过各导航路段、红绿灯以及红绿灯等待的时间之和；S53：通过调优计算公式计算得到浮动车速组中到达目的地的总体时间最短的对应车速，即为调优车速。
- 如权利要求3所述的基于无人驾驶的路径规划的方法，其特征在于，在步骤S53中采用的调优车速计算公式为：其中，tvi表示以速度v前进，到达第i个红绿灯所需时间，twi表示在第i个红绿灯所需的等待时间，tci表示横穿第i个红绿灯路口所需时间；f(v)表示求得使累计总时间最短的速度v的函数。
- 一种基于无人驾驶的路径规划的装置，其特征在于，包括以下模块：参数计算模块：用于根据获取到的车辆参数计算起步参数和刹车参数，并根据起步参数和刹车参数对车辆进行相应控制，该车辆参数包括起步加速度和刹车加速度，该起步参数包括起步距离和起步时间，该刹车参数包括刹车距离和刹车时间；导航信息获取模块：用于获取起点至目的地之间的导航道路信息，该导航道路信息包括导航路径、红绿灯信号和道路状况信息；车辆状态获取模块：用于获取车辆当前的行驶状态，该车辆当前的行驶状态包括车辆的当前位置信息和车辆的行驶速度；浮动车速计算模块：用于根据车辆的当前行驶状态和导航道路信息得到浮动车速组；调优车速模块：用于根据车辆的当前行驶状态和导航道路信息得到浮动车速组；车速控制模块：用于判断车辆的行驶速度与调优车速是否适应，如果否，则调整车速至调优车速。
- 如权利要求5所述的基于无人驾驶的路径规划的装置，其特征在于，所述导航信息获取模块具体包括以下子模块：导航路径获取模块：用于获取起点与目的地之间的导航路径；红绿灯识别模块：用于识别导航路径上所有的红绿灯信号，所述红绿灯信号包括红绿灯位置、红绿灯状态和红绿灯转换规则；导航路段切分模块：用于根据红绿灯位置将导航路径切分成多个路段以得到导航路段；道路信息获取模块：用于获取各个导航路段的道路状况信息，该道路状况信息包括平均车速和限速信息。
- 如权利要求5所述的基于无人驾驶的路径规划的装置，其特征在于，所述调优车速模块具体包括以下子模块：导航路段时间模块：用于根据浮动车速组、车辆的当前位置和道 路状况信息以计算浮动车速组中各车速通过各导航路段所需要的时间；时间计算模块：用于以浮动车速组中各车速计算通过各导航路段、红绿灯以及红绿灯等待的时间之和；调优车速计算模块：用于通过调优计算公式计算得到浮动车速组中到达目的地的总体时间最短的对应车速，即为调优车速。
- 如权利要求7所述的基于无人驾驶的路径规划的装置，其特征在于，在调优车速计算模块中采用的调优车速计算公式为：其中，tvi表示以速度v前进，到达第i个红绿灯所需时间，twi表示在第i个红绿灯所需的等待时间，tci表示横穿第i个红绿灯路口所需时间；f(v)表示求得使累计总时间最短的速度v的函数。
- 一种基于无人驾驶的路径规划的系统，其特征在于，包括执行器，所述执行器用于执行如权利要求1-4中任意一项所述的基于无人驾驶的路径规划的方法。
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| US20180004215A1 (en) * | 2017-09-15 | 2018-01-04 | GM Global Technology Operations LLC | Path planning of an autonomous vehicle for keep clear zones |
| CN108364494B (zh) * | 2018-02-26 | 2023-11-10 | 郑清辉 | 道路交通智能管理方法、系统及平台 |
| US20210003411A1 (en) * | 2018-03-07 | 2021-01-07 | Honda Motor Co., Ltd. | Vehicle control system, vehicle control method, medium, and information processing device |
| CN108597218A (zh) * | 2018-06-13 | 2018-09-28 | 桂林电子科技大学 | 基于车辆监控和车联网技术的路径规划与车辆速度优化装置 |
| CN112437951B (zh) * | 2018-08-17 | 2023-03-10 | 谷歌有限责任公司 | 减少交叉路口处的车辆拥堵 |
| CN109389838A (zh) * | 2018-11-26 | 2019-02-26 | 爱驰汽车有限公司 | 无人驾驶路口路径规划方法、系统、设备及存储介质 |
| CN109656248A (zh) * | 2018-11-30 | 2019-04-19 | 五方智能车科技有限公司 | 一种用于智能车路况分析的测试系统 |
| CN109597317B (zh) * | 2018-12-26 | 2022-03-18 | 广州小鹏汽车科技有限公司 | 一种基于自学习的车辆自动驾驶方法、系统及电子设备 |
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| CN109827588B (zh) * | 2019-03-08 | 2022-01-14 | 联想(北京)有限公司 | 一种处理方法及电子设备 |
| CN111380557A (zh) * | 2020-03-24 | 2020-07-07 | 李子月 | 一种无人车全局路径规划方法及装置 |
| CN113781818B (zh) * | 2020-06-08 | 2022-11-08 | 北京京东乾石科技有限公司 | 规划车辆行驶信息的方法和装置 |
| CN112258873B (zh) * | 2020-09-04 | 2022-10-28 | 阿波罗智联(北京)科技有限公司 | 用于控制车辆的方法、装置、电子设备及存储介质 |
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