An Obstacle Avoidance Method for Intelligent Connected Vehicles

Abstract

This application relates to the technical field of vehicle data processing, and particularly to an obstacle avoidance method and device for intelligent connected vehicles. The method includes: obtaining vehicle information of a target vehicle, a first position and a moving pose of an obstacle, where the vehicle information includes a second position and a vehicle speed; predicting the moving trajectory of the obstacle within a preset time period according to the moving pose; obtaining the obstacle category of the obstacle; determining the collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position and the moving trajectory; generating an obstacle avoidance strategy based on the vehicle speed and the collision probability, and controlling the target vehicle according to the obstacle avoidance strategy. This application improves the accuracy of vehicle obstacle avoidance effectively.

Description

Technical Field

[0001] This application relates to the technical field of image data processing, and particularly to an obstacle avoidance method for intelligent connected vehicles. Background Art

[0002] With the progress and development of technology, the automotive industry is also constantly developing towards the direction of intelligence and digitization. At the same time, autonomous driving and assisted driving technologies have been more and more widely applied. During the driving process of a vehicle, autonomous driving technology and assisted driving technology can help the vehicle avoid obstacles in a timely manner, and the safety of vehicle driving can be effectively improved by avoiding obstacles in a timely manner.

[0003] In related technologies, the image of the driving road surface and the vehicle position are obtained in real time, and the current obstacle position of the obstacle in the driving road surface is determined according to the driving road surface image. Then, the collision probability is determined according to the current obstacle position and the vehicle position. However, during the actual driving process, obstacles move suddenly, and different obstacles have different controllabilities. Therefore, it can be seen that the accuracy of determining the collision probability by only considering the current obstacle position in related technologies is poor. Summary of the Invention

[0004] In order to improve the accuracy of vehicle obstacle avoidance, the present invention provides an obstacle avoidance method for intelligent connected vehicles.

[0005] In a first aspect, the present application provides an obstacle avoidance method for intelligent connected vehicles, adopting the following technical solutions:

[0006] An obstacle avoidance method for intelligent connected vehicles includes:

[0007] Obtaining the vehicle information of the target vehicle, the first position and the moving pose of the obstacle, where the vehicle information includes the second position and the vehicle speed;

[0008] Predicting the moving trajectory of the obstacle within a preset time period according to the moving pose;

[0009] Obtaining the obstacle category of the obstacle;

[0010] Determining the collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position and the moving trajectory;

[0011] Generating an obstacle avoidance strategy based on the vehicle speed and the collision probability, and controlling the target vehicle according to the obstacle avoidance strategy.

[0012] In a preferred embodiment of the present application, the determining the collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position and the moving trajectory includes:

[0013] Determine the third position of the target vehicle based on the vehicle speed, the preset duration, and the second position;

[0014] Determine whether the obstacle arrival point is within the preset safety range of the third position, where the obstacle arrival point is the end point of the movement trajectory and is determined jointly by the first position and the movement trajectory;

[0015] If not, obtain driver information, and determine the collision probability between the obstacle and the target vehicle according to the driver information, the first position, the second position, and the obstacle category;

[0016] If so, determine the collision probability as the first preset collision probability.

[0017] In a preferred embodiment of the present application, it can be further configured that the driver information includes: the driving duration of the driver and the driver's age,

[0018] The determining the collision probability between the obstacle and the target vehicle according to the driver information, the first position, the second position, and the obstacle category includes:

[0019] Determine the reaction time of the driver based on the driver's age and the driving duration;

[0020] Determine the fourth position of the target vehicle based on the vehicle speed, the reaction time, and the second position;

[0021] Determine the fifth position of the obstacle based on the moving speed of the obstacle, the reaction time, and the first position;

[0022] Determine the first collision probability according to the fourth position and the fifth position;

[0023] Determine the second collision probability based on the first corresponding relationship and the obstacle category, where the first corresponding relationship is the corresponding relationship between the obstacle category and the collision probability;

[0024] Determine the collision probability between the obstacle and the target vehicle according to the first collision probability and the second collision probability.

[0025] In a preferred embodiment of the present application, it can be further configured that the determining the reaction time of the driver based on the driver's age and the driving duration includes:

[0026] Determine the reaction time corresponding to the driver's age based on the driver's age and the second corresponding relationship, where the second corresponding relationship is the corresponding relationship between the driver's age and the reaction time;

[0027] Determine a correction coefficient for correcting the reaction time according to the driving duration;

[0028] Use the correction coefficient to correct the reaction time corresponding to the driver's age to obtain the driver's reaction time.

[0029] In a preferred embodiment of the present application, it can be further configured that determining the first collision probability based on the fourth position and the fifth position includes:

[0030] Determine a danger range corresponding to the fifth position of the obstacle, and determine whether the fourth position of the target vehicle is within the danger range;

[0031] If it is within the danger range, determine the preset second collision probability as the first collision probability;

[0032] If it is not within the danger range, determine the preset third collision probability as the first collision probability.

[0033] In a preferred embodiment of the present application, it can be further configured that generating an obstacle avoidance strategy based on the vehicle speed and the collision probability includes:

[0034] Determine whether the collision probability is greater than a preset collision probability threshold;

[0035] If so, control the vehicle speed to change to a first safe speed, and generate the obstacle avoidance strategy based on the first safe speed;

[0036] If not, obtain the sixth position of the vehicle in the adjacent lane, and determine the lane-changing distance of the target vehicle based on the sixth position and the second position;

[0037] Obtain a second safe speed, and generate an obstacle avoidance strategy according to the lane-changing distance and the second safe speed.

[0038] In summary, the present application includes the following beneficial technical effects:

[0039] Obtain the vehicle information of the target vehicle, the first position and the moving pose of the obstacle; during actual driving, the target vehicle is in a dynamic environment, and obstacle avoidance according to dynamic changes can effectively improve the accuracy of the obstacle avoidance probability. Therefore, it is necessary to predict the moving trajectory according to the moving pose in order to combine the main dynamic change factors for obstacle avoidance; then obtain the obstacle category of the obstacle. Different types of obstacles have different controllabilities during driving, so it is necessary to obtain the obstacle category; and when the distance between the target vehicle and the obstacle is closer, the collision probability is higher. Therefore, based on the obstacle category, the first position, the second position and the moving trajectory, a more accurate collision probability can be obtained, and a more reasonable obstacle avoidance strategy can be generated according to the accurate collision probability and the vehicle speed, and the target vehicle can be controlled according to the obstacle avoidance strategy; compared with the related technology, the present application analyzes the obstacle in different dimensions to determine the collision probability, solving the problem of poor accuracy of the collision probability in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic flowchart of a method for obstacle avoidance of an intelligent connected vehicle provided by an embodiment of the present application;

[0041] Figure 2 It is a schematic structural diagram of an obstacle avoidance device for an intelligent connected vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will further describe the present application in detail Figure 1 in conjunction with the appended Figure 2 drawings.

[0043] Those skilled in the art can make modifications to this embodiment without creative contributions according to needs after reading this specification, but as long as it is within the scope of the claims of the present application, it is protected by the patent law.

[0044] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0045] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0046] The following further describes the embodiments of the present application in conjunction with the accompanying drawings of the specification.

[0047] The embodiments of the present application provide an obstacle avoidance method for intelligent connected vehicles, which is executed by an in-vehicle electronic device, and the in-vehicle electronic device is a terminal device. The terminal device can be indirectly or directly connected through wired or wireless communication methods, which are not limited in the embodiments of the present application. For example Figure 1 The following is a schematic flowchart of an obstacle avoidance method for intelligent connected vehicles provided by the embodiments of the present application. The method includes steps S1, S2, S3, S4, and S5, where:

[0048] Step S1: Obtain the vehicle information of the target vehicle, the first position and the moving pose of the obstacle. The vehicle information includes the second position and the vehicle speed.

[0049] Specifically, when the in-vehicle electronic device detects the start of the target vehicle, it obtains the vehicle information of the target vehicle, the first position of the obstacle, and the moving pose of the obstacle. An image acquisition device is installed in the target vehicle, and the image acquisition device can collect the moving pose of the obstacle and upload it to the in-vehicle electronic device. The second position and the vehicle speed can be detected by a position sensor and a vehicle speed sensor respectively and uploaded to the in-vehicle electronic device. In the embodiments of the present application, the moving pose can be the direction of the obstacle.

[0050] Step S2: Predict the moving trajectory of the obstacle within a preset time period according to the moving pose.

[0051] Specifically, the preset time period is preset by technicians according to work experience and can be 1 second, 1.5 seconds, 3 seconds, etc. The preset time period is the maximum reaction time of drivers of all ages and driving durations in the actual scenario after seeing the obstacle. That is, after seeing this obstacle, the driver will make an obstacle avoidance reaction within this preset time period. Among them, the moving trajectory of the obstacle can be predicted through a time series neural network model and the current moving speed of the obstacle. The embodiments of the present application do not limit the specific process of predicting the moving trajectory of the obstacle.

[0052] Step S3: Obtain the obstacle category of the obstacle.

[0053] Specifically, the obstacle categories include: the first category, the second category, and the third category. Among them, the first category can be stationary objects, such as vehicles and markers stationary on the road. When it is the first category, the corresponding movement trajectory of the obstacle is 0, that is, there is no movement trajectory; the second category can be moving young children (humans under six years old or with a height below 130 cm), animals, and non-motor vehicles, and the third category is moving motor vehicles and non-young children. The vehicle-mounted electronic device identifies the received obstacle image to determine the obstacle category, and the embodiments of the present application do not limit the specific identification process.

[0054] Step S4: Determine the collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position, and the movement trajectory.

[0055] Specifically, the specific process of determining the collision probability according to the obstacle category, the first position, the second position, and the movement trajectory can refer to the following embodiments. It can be understood that different obstacle categories have different collision probabilities with the target vehicle. For example, a stationary marker and a moving young child. During the driving of the target vehicle, the stationary marker will not move, so the target vehicle only needs to adjust the vehicle position in time to avoid collision; in comparison, a moving young child has greater uncertainty during driving, and at this time the collision probability between the obstacle and the target vehicle will increase greatly. When the movement trajectory of the moving young child coincides with the forward route of the target vehicle, its collision probability will further increase; and when the distance between the target vehicle and the obstacle is closer, the collision probability will also increase accordingly. The closer the distance between the obstacle and the target vehicle, the higher the coincidence rate of the movement trajectory of the obstacle and the target vehicle, and at this time the collision probability also increases. Therefore, a more accurate collision probability can be obtained according to the obstacle category, the first position, the second position, and the movement trajectory.

[0056] Further, determining the collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position, and the movement trajectory includes:

[0057] Determine the third position of the target vehicle according to the vehicle speed, the preset duration, and the second position;

[0058] Judge whether the obstacle arrival point is within the preset safety range of the third position. The obstacle arrival point is the end point of the movement trajectory, which is jointly determined by the first position and the movement trajectory;

[0059] If not, obtain the driver information, and determine the collision probability between the obstacle and the target vehicle according to the driver information, the first position, the second position, and the obstacle category;

[0060] If so, determine the collision probability as the first preset collision probability.

[0061] Specifically, according to the vehicle speed and a preset duration, the first forward distance of the target vehicle is obtained, and then according to the first forward distance and the second position, the third position, that is, the position after the target vehicle moves, is obtained. The preset safety range of the third position is preset. When the obstacle arrival point is within the preset safety range, it indicates that the obstacle is within the safety range of the third position. At this time, the vehicle will not collide with the obstacle, that is, the first preset collision probability is 0 (in other words, assuming that a driver makes a reaction within the maximum reaction duration, there is still a safety range between the vehicle and the obstacle, then it is almost impossible for the vehicle to collide with the obstacle); otherwise, it indicates that a collision with the obstacle may occur. At this time, it is necessary to predict the reaction duration for this specific driver based on the driver information, and re-determine the possible positions of the obstacle and the target vehicle within this reaction duration, and then determine an accurate collision probability in combination with the obstacle category.

[0062] Based on the above embodiments, when the moving speed of the obstacle and the vehicle speed are faster and the distance between them is closer, the collision time until the collision occurs is shorter, and as the collision time decreases, the collision probability increases. Therefore, it is necessary to determine the third position of the target vehicle and judge whether the obstacle arrival point is within the preset safety range of the third position. When the obstacle arrival point is within the preset safety range, it indicates that the collision probability between the target vehicle and the obstacle is extremely small, so it is directly determined as the first preset collision probability; if the obstacle arrival point is not within the preset safety range, it indicates that there is a relatively high collision probability. Therefore, it is necessary to combine the driver information, the first position, the second position, and the obstacle category to obtain a more accurate collision probability.

[0063] Further, determining the collision probability between the obstacle and the target vehicle according to the driver information, the first position, the second position, and the obstacle category includes:

[0064] Based on the driver's age and driving duration, determine the driver's reaction duration;

[0065] Based on the vehicle speed, the reaction duration, and the second position, determine the fourth position of the target vehicle;

[0066] Based on the moving speed of the obstacle, the reaction duration, and the first position, determine the fifth position of the obstacle;

[0067] According to the fourth position and the fifth position, determine the first collision probability;

[0068] Based on the first correspondence relationship and the obstacle category, determine the second collision probability, where the first correspondence relationship is the correspondence relationship between the obstacle category and the collision probability;

[0069] According to the first collision probability and the second collision probability, determine the collision probability between the obstacle and the target vehicle.

[0070] Specifically, as the driver's age increases, the reaction time of the driver will increase accordingly. As the driving duration increases, the driver becomes more fatigued, and the reaction time of the driver also increases. As the reaction time increases, the driver's ability to handle emergencies becomes worse, and dangerous accidents are more likely to occur, that is, the collision probability increases accordingly. Therefore, it is necessary to determine the driver's reaction time based on the driver's age and driving duration. The specific process of determining the reaction time includes: determining the reaction time corresponding to the driver's age according to the driver's age and the second corresponding relationship. The second corresponding relationship is the corresponding relationship between the driver's age and the reaction time, and the second corresponding relationship is established by technicians. The specific process includes: obtaining multiple pieces of first reference driver information, where the first reference driver information includes the number of first reference drivers and their respective corresponding test reaction times; calculating the total sum of the test reaction times based on all the test reaction times, and then determining the average reaction time according to the total sum of the test reaction times and the number of drivers, and determining the average reaction time as the reaction time corresponding to the driver's age; then determining the correction coefficient corresponding to the above driving duration according to the corresponding relationship between the driving duration and the correction coefficient. It can be understood that the increase in the driving duration will increase the driver's fatigue, and at the same time the reaction time increases. Therefore, it is necessary to correct the reaction time. When the driving duration is longer, the correction coefficient is larger; the reaction time = the reaction time corresponding to the driver's age * the correction coefficient. Determining the second forward distance of the vehicle according to the vehicle speed and the reaction time, and then obtaining the fourth position according to the second forward distance and the second position; determining the third forward distance of the obstacle according to the moving speed of the obstacle and the reaction time, and then obtaining the fifth position according to the third forward distance and the first position; then determining the first collision probability according to the fourth position and the fifth position includes:

[0071] Determining the dangerous range corresponding to the fifth position of the obstacle, and determining whether the fourth position of the target vehicle is within the dangerous range;

[0072] If it is within the dangerous range, then determining the preset second collision probability as the first collision probability;

[0073] If it is not within the dangerous range, then determining the preset third collision probability as the first collision probability.

[0074] A circle can be established with the fifth position of the obstacle as the center and a preset distance (pre-set by technicians) as the radius, and the range covered by this circle is determined as the dangerous range. It can be understood that during actual driving, the obstacle may slide due to being out of control or falling down. Therefore, considering the occurrence of sliding, it is necessary to determine the dangerous range of the obstacle. If the fourth position of the target vehicle is within the dangerous range, the preset second collision probability is determined as the first collision probability, such as 80% or 70%, etc.; if it is not within the dangerous range, it is determined as the preset third collision probability, such as 10% or 15%, etc. The embodiments of the present application do not limit specific values, and the preset second collision probability is greater than the preset third collision probability.

[0075] Further, the second collision probability is determined by the first corresponding relationship (the corresponding relationship between the obstacle category and the collision probability). The corresponding relationship between the obstacle category and the collision probability is set by technicians according to multiple historical data and pre-input into the vehicle-mounted electronic device, and then the second collision probability can be determined. In the embodiments of the present application, the second collision probability of the first category < the second collision probability of the third category < the second collision probability of the second category. Calculate the total collision probability of the first collision probability and the second collision probability, and determine the total collision probability as the collision probability. It can be understood that when the driver's reaction time is long and the obstacle category is the second category or the third category, the collision probability will increase greatly. Therefore, the total collision probability is determined as the collision probability between the obstacle and the target vehicle, so as to effectively improve the accuracy of determining the collision probability.

[0076] Step S5: Generate an obstacle avoidance strategy based on the vehicle speed and the collision probability, and control the target vehicle according to the obstacle avoidance strategy.

[0077] Specifically, the specific process of generating an obstacle avoidance strategy according to the vehicle speed and the collision probability can be referred to as follows. Then generate a corresponding control signal according to the obstacle avoidance strategy, and send the control signal to the corresponding device to control the target vehicle, reduce the collision probability between the target vehicle and the obstacle, and minimize the loss.

[0078] Further, generating an obstacle avoidance strategy based on the vehicle speed and the collision probability includes:

[0079] Judge whether the collision probability is greater than the preset collision probability threshold;

[0080] If so, control the vehicle speed to change to the first safe speed, and generate an obstacle avoidance strategy based on the first safe speed;

[0081] If not, obtain the sixth position of the vehicle in the adjacent lane, and determine the lane-changing distance of the target vehicle based on the sixth position and the second position;

[0082] Obtain the second safe speed, and generate an obstacle avoidance strategy based on the lane-changing distance and the second safe speed.

[0083] Specifically, both the preset collision probability threshold and the second safe speed are set by technicians according to work experience; the first safe speed is 0; when the collision probability is greater than the preset collision probability threshold, it indicates that the collision probability between the obstacle and the target vehicle is relatively large, and there may be a serious impact with the obstacle. To minimize the damage to the obstacle, it is necessary to control the target vehicle to stop moving forward to avoid the obstacle. If not, it indicates that the collision probability between the obstacle and the target vehicle is relatively small, and obstacle avoidance can be achieved by deviating from the original driving lane, that is, driving into the adjacent lane. Before the target vehicle drives into the adjacent lane, it is also necessary to consider whether there will be a collision with the vehicles in the adjacent lane to avoid danger to the driver of the target vehicle caused by the vehicles in the adjacent lane. Therefore, it is necessary to obtain the sixth position of the vehicles in the adjacent lane. The acquisition method of the sixth position is the same as that of the first position of the obstacle, and it will not be elaborated in this embodiment of the present application. The process of determining the lane-changing distance based on the sixth position and the second position includes: determining the distance difference based on the second position and the sixth position; and determining the lane-changing distance according to the distance difference and the corresponding relationship; the distance difference can be obtained according to the Euclidean distance calculation formula, and the above corresponding relationship is the corresponding relationship between the distance difference and the lane-changing distance preset by technicians; it can be understood that as the distance difference increases, the lane-changing distance becomes larger. The in-vehicle electronic device sends the preset safe driving speed to the vehicle speed control system so that the vehicle speed of the target vehicle drops to the preset safe driving speed, and at the same time sends the lane-changing distance to the vehicle steering wheel control system so as to control the target vehicle to drive into the adjacent lane at the preset safe driving speed.

[0084] Based on the above embodiments, judge the collision probability and the preset collision probability threshold. When the collision probability is greater than the preset collision probability threshold, it indicates that there is a high probability that the obstacle and the target vehicle will have a serious collision. At this time, directly control the vehicle speed to be the first safe speed and stop the vehicle to avoid a collision between the target vehicle and the obstacle; when the collision probability is not greater than the preset collision probability threshold, it indicates that the probability of a frontal collision between the obstacle and the target vehicle is relatively small. At this time, controlling the target vehicle to drive into other lanes can reduce the damage to the obstacle and the danger caused by the target vehicle stopping. Therefore, it is necessary to obtain the fourth position in the adjacent lane to ensure the rationality of the lane-changing distance, and then generate an obstacle avoidance strategy based on the lane-changing distance and the second safe speed, which effectively improves the rationality of the obstacle avoidance strategy.

[0085] Based on the above embodiments, obtain the vehicle information of the target vehicle, the first position and the moving pose of the obstacle; during actual driving, the target vehicle is in a dynamic environment, and obstacle avoidance according to dynamic changes can effectively improve the accuracy of the obstacle avoidance probability. Therefore, it is necessary to predict the moving trajectory according to the moving pose in order to combine the main dynamic change factors for obstacle avoidance; then obtain the obstacle category of the obstacle. Different types of obstacles have different controllabilities during driving, so it is necessary to obtain the obstacle category; and when the distance between the target vehicle and the obstacle is closer, the collision probability is higher. Therefore, according to the obstacle category, the first position, the second position and the moving trajectory, a more accurate collision probability can be obtained. Then, a more reasonable obstacle avoidance strategy can be generated according to the accurate collision probability and the vehicle speed, and the target vehicle can be controlled according to the obstacle avoidance strategy; compared with the related art, the present application analyzes the obstacle in different dimensions to determine the collision probability, and solves the problem of poor accuracy of the collision probability in the related art.

[0086] The above are only partial embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. An obstacle avoidance method for intelligent connected vehicles, characterized in that Including: Obtaining vehicle information of a target vehicle, a first position and a moving pose of an obstacle, where the vehicle information includes a second position and a vehicle speed; Predicting a moving trajectory of the obstacle within a preset time period according to the moving pose; Obtaining an obstacle category of the obstacle; Determining a collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position and the moving trajectory; Generating an obstacle avoidance strategy based on the vehicle speed and the collision probability, and controlling the target vehicle according to the obstacle avoidance strategy; The determining the collision probability between the obstacle and the target vehicle according to the obstacle category, the first position, the second position and the moving trajectory includes: Determining a third position of the target vehicle based on the vehicle speed, the preset time period and the second position; Judging whether an obstacle arrival point is within a preset safety range of the third position, where the obstacle arrival point is an end point of the moving trajectory and is jointly determined by the first position and the moving trajectory; If not, obtaining driver information, and determining the collision probability between the obstacle and the target vehicle according to the driver information, the first position, the second position and the obstacle category; If so, determining the collision probability as a first preset collision probability; The driver information includes: driving duration of the driver and driver age, The determining the collision probability between the obstacle and the target vehicle according to the driver information, the first position, the second position and the obstacle category includes: Determining a reaction duration of the driver based on the driver age and the driving duration; Determining a fourth position of the target vehicle based on the vehicle speed, the reaction duration and the second position; Determining a fifth position of the obstacle based on a moving speed of the obstacle, the reaction duration and the first position; Determining a first collision probability according to the fourth position and the fifth position; Determining a second collision probability based on a first corresponding relationship and the obstacle category, where the first corresponding relationship is a corresponding relationship between an obstacle category and a collision probability; Determining the collision probability between the obstacle and the target vehicle according to the first collision probability and the second collision probability; The determining the first collision probability based on the fourth position and the fifth position includes: Determining a danger range corresponding to the fifth position of the obstacle, and judging whether the fourth position of the target vehicle is within the danger range; If it is within the danger range, determining a preset second collision probability as the first collision probability; If it is not within the danger range, determining a preset third collision probability as the first collision probability.

2. The method for obstacle avoidance of an intelligent connected vehicle according to claim 1, characterized in that, The determining the reaction duration of the driver based on the driver age and the driving duration includes: Determining a reaction duration corresponding to the driver age based on the driver age and a second corresponding relationship, where the second corresponding relationship is a corresponding relationship between a driver age and a reaction duration; Determining a correction coefficient for correcting the reaction duration according to the driving duration; Use the correction coefficient to correct the reaction time corresponding to the driver's age to obtain the reaction time of the driver.

3. The intelligent networked vehicle obstacle avoidance method according to claim 1, characterized in that The generating an obstacle avoidance strategy based on the vehicle speed and the collision probability includes: Determine whether the collision probability is greater than a preset collision probability threshold; If so, control the vehicle speed to change to a first safe speed and generate the obstacle avoidance strategy based on the first safe speed; If not, obtain the sixth position of the vehicle in the adjacent lane, and determine the lane-changing distance of the target vehicle based on the sixth position and the second position; Obtain a second safe speed, and generate an obstacle avoidance strategy according to the lane-changing distance and the second safe speed.

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