Information prompting method and device of vehicle, computer readable storage medium and vehicle
By identifying the types of vehicles in the same lane and dynamically adjusting the safe distance, the problem of false alarms and missed alarms in existing vehicle collision warning systems in complex environments has been solved. This enables accurate identification and risk warning of large vehicle-encircling scenarios, thereby improving vehicle driving safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG GEELY HLDG GRP CO LTD
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-03
AI Technical Summary
Existing vehicle collision warning systems are prone to false alarms or missed alarms in complex road environments, leading to safety risks. In particular, they cannot effectively identify and alert to high-risk scenarios such as large vehicles being sandwiched in complex road conditions.
By collecting real-time data on other vehicles traveling in the same lane as the vehicle, identifying their types, and dynamically adjusting the target safe distance of the collision warning system based on the speed of the large vehicle and the current vehicle, the system outputs corresponding prompts by comparing the distances of vehicles in front and behind.
It reduces false alarms or missed alarms in collision warning systems, improves vehicle driving safety, and can accurately identify and warn of risks, especially in high-risk scenarios such as being sandwiched by large vehicles, thus reducing the possibility of vehicles being crushed.
Smart Images
Figure CN122337033A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of vehicle safety assistance technology, and in particular to a vehicle information prompting method and device, a computer-readable storage medium, and a vehicle. Background Technology
[0002] In related technologies, vehicle safety assistance typically includes forward collision warning systems, blind spot monitoring systems, and adaptive cruise control systems. The forward collision warning system uses millimeter-wave radar or cameras to detect the distance between the vehicle and the vehicle in front. When the distance between the vehicle and the vehicle in front is less than a preset safe distance, an alarm is triggered.
[0003] This method of triggering alarms based solely on a fixed distance is ineffective in certain scenarios, especially in complex road environments, where it can easily lead to false alarms or missed alarms, posing a safety risk to vehicles. Summary of the Invention
[0004] To address the aforementioned technical problems, this disclosure provides a vehicle information prompting method and apparatus, a computer-readable storage medium, and a vehicle, which can reduce false alarms or missed alarms in vehicle collision warning systems and improve vehicle driving safety.
[0005] Firstly, this disclosure provides a method for displaying vehicle information, the method comprising: During the vehicle's journey, obtain the vehicle type and initial speed of the target vehicle traveling in the same lane as the vehicle. If the vehicle type matches the target type, the target safety distance corresponding to the target vehicle is determined based on the vehicle's second and first driving speeds. Based on the target safe distance, the system compares the distance between the vehicle and the target vehicle and outputs the corresponding prompt information.
[0006] Optionally, based on the target safety distance and the distance comparison result between the vehicle and the target vehicle, the corresponding prompt information includes: When the target vehicle includes both a preceding vehicle and a following vehicle, the target risk level is determined based on a comparison between a first distance and a first safe distance, and a comparison between a second distance and a second safe distance. Here, the preceding vehicle is the vehicle located in front of the vehicle in the direction of travel, and the following vehicle is the vehicle located behind the vehicle in the direction of travel. The first distance is the distance between the vehicle and the preceding vehicle, and the first safe distance is the target safe distance corresponding to the preceding vehicle. The second distance is the distance between the vehicle and the following vehicle, and the second safe distance is the target safe distance corresponding to the following vehicle. Output the target risk level and the corresponding prompt information.
[0007] In some embodiments of this disclosure, optionally, the target risk level is determined based on a comparison between a first distance and a first safe distance, and a comparison between a second distance and a second safe distance, including: If the first distance is less than or equal to the first safety distance, or the second distance is less than or equal to the second safety distance, the target risk level is determined to be medium risk level; If the first distance is less than or equal to the first safety distance and the second distance is less than or equal to the second safety distance, the target risk level is determined to be high risk level.
[0008] In some embodiments of this disclosure, optionally, determining the target safety distance corresponding to the target vehicle based on the vehicle's second driving speed and first driving speed includes: The relative speed between the vehicle and the target vehicle is obtained based on the difference between the second driving speed and the first driving speed. The first risk coefficient is determined based on the ratio of the target risk constant to the relative speed. The second risk factor is determined based on the ratio of the second driving speed to the reference speed; Based on the mapping relationship between environmental information and risk coefficients, the third risk coefficient corresponding to the vehicle's environmental information is determined; environmental information includes at least one of weather information and road information. The target safety distance is determined by multiplying the initial safety distance, the first risk coefficient, the second risk coefficient, and the third risk coefficient.
[0009] In some embodiments of this disclosure, optionally, obtaining the vehicle type of a target vehicle traveling in the same lane as the vehicle includes: Acquire detection information of the target vehicle; the detection information includes image information, point cloud information, and reflection information; The detection information is input into the pre-trained detection model to obtain the vehicle type of the target vehicle. The pre-trained detection model extracts features from the detection information to obtain the feature information of the target vehicle, processes the feature information, and outputs the vehicle type.
[0010] In some embodiments of this disclosure, optionally, obtaining the vehicle type of a target vehicle traveling in the same lane as the vehicle includes: Obtain the vehicle dimensions of the target vehicle; Based on the mapping relationship between vehicle size and vehicle type, the vehicle type of the target vehicle is determined.
[0011] Optionally, in some embodiments of this disclosure, the method further includes: Obtain road information; In response to the driver's confirmation of the prompt, a lane change route is generated based on road information; Lane change path control controls the vehicle's change of lane.
[0012] Secondly, embodiments of this disclosure provide a vehicle information display device, the device comprising: The acquisition module is used to acquire the vehicle type and first speed of a target vehicle traveling in the same lane as the vehicle during the vehicle's operation. The determination module is used to determine the target safety distance corresponding to the target vehicle based on the vehicle's second and first driving speeds, provided that the vehicle type matches the target type. The output module is used to output corresponding prompt information based on the distance comparison results between the target safe distance and the vehicle and the target vehicle.
[0013] Thirdly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the vehicle information prompting method provided in the first aspect.
[0014] Fourthly, embodiments of this disclosure provide a vehicle, including: Memory; Processor; and Computer programs; The computer program is stored in memory and configured to be executed by a processor to implement the steps of the vehicle information prompting method as provided in the first aspect.
[0015] The technical solution provided in this disclosure has the following advantages compared with the prior art: This embodiment of the disclosure collects real-time data on other vehicles traveling in the same lane as the vehicle and identifies the vehicle type of these vehicles. When a target type of vehicle, such as a large vehicle, is identified in front or behind in the same lane, the target safety distance of the vehicle collision warning system is dynamically adjusted based on the first speed of the large vehicle and the second speed of the current vehicle. By dynamically adjusting the target safety distance, the vehicle collision warning system can adapt to various road conditions, reduce false alarms or missed alarms, and improve vehicle driving safety. Attached Figure Description
[0016] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 One of the flowcharts illustrating a vehicle information prompting method according to some embodiments of this disclosure is shown; Figure 2 A second flowchart illustrating a vehicle information prompting method according to some embodiments of the present disclosure is shown; Figure 3 One of the flowcharts illustrating the determination of the vehicle type of a target vehicle according to some embodiments of this disclosure is shown; Figure 4 A second flowchart illustrating the determination of the vehicle type of a target vehicle according to some embodiments of this disclosure is shown; Figure 5 A flowchart illustrating a vehicle anti-pinch intervention method according to some embodiments of the present disclosure is shown; Figure 6 A flowchart illustrating an anti-clamping control method according to some embodiments of the present disclosure is shown; Figure 7 A structural block diagram of a vehicle information display device according to some embodiments of the present disclosure is shown. Detailed Implementation
[0019] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0020] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.
[0021] Some of the nouns or terms that appear in the description of the embodiments of this disclosure shall be interpreted as follows: FCW: Forward Collision Warning.
[0022] BSD stands for Blind Spot Detection.
[0023] ACC: Adaptive Cruise Control.
[0024] Before describing the embodiments of this disclosure in detail, the technical background involved in this disclosure will be described here so that those skilled in the art can have a clearer understanding of the embodiments of this disclosure.
[0025] In related technologies, vehicle safety assistance systems can detect obstacles in the direction of vehicle travel and provide targeted prompts to the driver based on the detection results.
[0026] For example, FCW, or Forward Collision Warning System. FCW uses millimeter-wave radar or visual sensors such as sound sensors to detect the distance between the current vehicle and the vehicle or obstacle in front. When the detected distance is less than a preset safe distance threshold, it issues a collision warning to the driver. FCW focuses its detection attention on the area in front of the vehicle and does not pay attention to other directions, such as behind the vehicle.
[0027] For example, BSD, or Blind Spot Detection System. BSD uses rear-mounted ultrasonic or millimeter-wave radar to detect other vehicles or obstacles to the side and rear of the vehicle. When a vehicle or obstacle is detected within the blind spot, it issues an audible and visual warning. BSD can only detect the presence of obstacles within the blind spot; it cannot identify the state of the obstacles.
[0028] For example, ACC, or Adaptive Cruise Control. Adaptive Cruise Control can detect the speed difference between the current vehicle and the vehicle in front in real time, and adjust the current vehicle's speed accordingly to maintain a fixed distance between the two vehicles. ACC, on the other hand, cannot recognize the status of the vehicle in front. Regardless of what kind of vehicle it is, such as a large vehicle, a special vehicle, or a non-motorized vehicle, ACC will always maintain the same distance between the current vehicle and the vehicle in front.
[0029] The above solutions are all designed for very limited scenarios and cannot provide adaptive safety warnings in complex road environments. They are prone to false alarms or missed alarms, which can lead to safety risks for vehicles.
[0030] To address the aforementioned issues, this disclosure provides a method for displaying vehicle information. Figure 1 One of the flowcharts illustrating a vehicle information prompting method according to some embodiments of this disclosure is shown, such as... Figure 1 As shown, the method includes: S102, during the vehicle's travel, obtain the vehicle type and first travel speed of the target vehicle traveling in the same lane as the vehicle.
[0031] In this context, "vehicle" refers to the current vehicle, and "target vehicle" refers to other vehicles traveling in the same lane as the current vehicle. For example, the target vehicle can be the vehicle in front of the current vehicle, or it can be the vehicle behind the current vehicle.
[0032] For example, image sensors, ultrasonic radar, millimeter-wave radar, or lidar can be used to detect whether there is a target vehicle in front of and behind the vehicle, as well as the distance between the target vehicle and the current vehicle.
[0033] For example, the first speed of the target vehicle can be calculated by combining the real-time speed of the current vehicle with the distance change between the target vehicle and the current vehicle within a preset time period.
[0034] For example, the initial speed of a target vehicle can be detected by ultrasonic radar or millimeter-wave radar.
[0035] S104, if the vehicle type matches the target type, determine the target safety distance corresponding to the target vehicle based on the vehicle's second driving speed and first driving speed.
[0036] For example, the target type of vehicle is a "large vehicle". For example, the target type of vehicle, i.e., a large vehicle, can be a vehicle whose length exceeds a preset length, whose height exceeds a preset height, whose wheelbase exceeds a preset wheelbase, or whose weight exceeds a preset weight.
[0037] For example, the target type of vehicle can also be a preset model of vehicle, such as a certain model of truck, a certain model of engineering vehicle, etc.
[0038] Taking a large vehicle as an example, other vehicles on the road can affect the vehicle's driving safety. For instance, when a vehicle is following a large vehicle, or when a large vehicle is following behind it, the braking distance required by the large vehicle is much longer than that of a regular passenger car due to its greater mass and inertia. Furthermore, in the event of a collision, the greater mass and rigidity of the large vehicle will result in more severe damage to the vehicle involved and a higher likelihood of injury to the driver.
[0039] Therefore, when the vehicle type of the target vehicle is detected to match the target type, that is, the target vehicle is a large vehicle, the target safe distance between the current vehicle and the target vehicle is dynamically determined based on the second driving speed of the current vehicle and the first driving speed of the target vehicle to meet the safe driving requirements.
[0040] For example, a suitable target safety distance can be calculated based on whether the target vehicle is located in front of or behind the current vehicle, the difference between the second and first driving speeds, and the current vehicle speed.
[0041] For example, if the target vehicle is in front of the current vehicle, i.e., the current vehicle is following the target vehicle, and if the current vehicle's second speed is greater than the target vehicle's first speed, it means the current vehicle is approaching the target vehicle. At this point, the driver is likely focused and preparing to overtake. To avoid interfering with the driver's normal driving, a smaller safe distance from the target vehicle can be determined.
[0042] For example, if the target vehicle is behind the current vehicle, i.e., the current vehicle is being followed by a large vehicle, and if the current vehicle's second speed is less than the target vehicle's first speed, it indicates that a large vehicle is approaching from behind. In this case, a larger target safety distance can be determined, thus alerting the driver earlier that a large vehicle is approaching from behind.
[0043] For example, if the target vehicle is in front of the current vehicle, i.e., the current vehicle is following the target vehicle, and if the current vehicle's second speed equals the target vehicle's first speed, it means the current vehicle is following at a constant speed. This is a driving scenario where the driver is more likely to experience fatigue and distraction. Therefore, a larger safe distance can be determined to prevent following too closely.
[0044] S106, based on the target safe distance and the distance comparison result between the vehicle and the target vehicle, output the corresponding prompt information.
[0045] In this embodiment of the disclosure, after detecting a target vehicle of the same type in the same lane as the current vehicle, and dynamically determining the corresponding target safety distance based on the second driving speed of the current vehicle and the first driving speed of the target vehicle, the vehicle monitors the distance between itself and the target vehicle in real time and compares whether the distance is less than or equal to the target safety distance.
[0046] If the distance between the vehicle and the target vehicle is detected to be less than the target safe distance, the vehicle will output a corresponding prompt message to the driver to remind the driver to pay attention to driving safety.
[0047] For example, outputting a prompt message can be done by playing a corresponding prompt sound or voice message.
[0048] For example, outputting a prompt message could be controlling the vehicle's steering wheel to vibrate at a preset frequency.
[0049] For example, the output of the prompt message may be displayed on the vehicle's infotainment display, instrument panel, or head-up display, or the corresponding warning light on the instrument panel may be illuminated.
[0050] For example, in some implementations, when a vehicle detected in the lane in which the vehicle is traveling, such as the vehicle in front or behind, does not match the target type (i.e., it is not a large vehicle), the target safe distance can be determined based on the vehicle's current speed. For example, under the same speed conditions, if the vehicle in the same lane is not the target type, the determined target safe distance is less than the safe distance when the vehicle in the same lane is the target type.
[0051] This embodiment of the disclosure collects real-time data on other vehicles traveling in the same lane as the vehicle and identifies the vehicle type of these vehicles. When a target type of vehicle, such as a large vehicle, is identified in front or behind in the same lane, the target safety distance of the vehicle collision warning system is dynamically adjusted based on the first speed of the large vehicle and the second speed of the current vehicle. By dynamically adjusting the target safety distance, the vehicle collision warning system can adapt to various road conditions, reduce false alarms or missed alarms, and improve vehicle driving safety.
[0052] In some embodiments of this disclosure, specific driving situations are addressed, such as when large vehicles are positioned in front of and behind a vehicle, i.e., the vehicle is "sandwiched" between them. Due to the characteristics of large vehicles, the safety risks of a vehicle being sandwiched between them are significantly increased. For example, if the large vehicle in front brakes suddenly, and the large vehicle behind fails to brake in time, the large vehicle behind may rear-end the vehicle in front, pushing it onto the large vehicle in front. In this situation, the vehicle in front will fully bear the kinetic energy of the collision between the two large vehicles, resulting in severe damage to the vehicle and seriously threatening the lives of the occupants.
[0053] The forward collision warning system in related technologies only considers the distance between the current vehicle and the vehicle in front, without considering whether there is a "sandwich" situation. This results in a gap in the warning and avoidance of the "sandwich" risk, which increases the risk.
[0054] To address the lack of clear risk warnings regarding being "sandwiched" by large vehicles, alternatively, Figure 2 A second flowchart illustrating a vehicle information prompting method according to some embodiments of this disclosure is shown, such as... Figure 2 As shown, based on the target safe distance, the comparison results of the distance between the vehicle and the target vehicle, and the corresponding prompt information, include: S202, when the target vehicle includes both the vehicle in front and the vehicle behind, the target risk level is determined based on the comparison between the first distance and the first safe distance, and the comparison between the second distance and the second safe distance.
[0055] Wherein, the preceding vehicle is the vehicle located in front of the vehicle in the direction of travel, and the following vehicle is the vehicle located behind the vehicle in the direction of travel; the first distance is the distance between the vehicle and the preceding vehicle, and the first safe distance is the target safe distance corresponding to the preceding vehicle; the second distance is the distance between the vehicle and the following vehicle, and the second safe distance is the target safe distance corresponding to the following vehicle.
[0056] For example, taking a large vehicle as the target vehicle, when the target vehicle is detected to include both the vehicle in front and the vehicle behind, it means that the vehicle is currently sandwiched between two large vehicles. At this time, the vehicle determines a first safe distance between itself and the vehicle in front based on its second driving speed and the first driving speed of the vehicle in front, and simultaneously determines a second safe distance between itself and the vehicle behind based on its second driving speed and the first driving speed of the vehicle behind.
[0057] For example, the safe distance is greater when both the vehicle in front and the vehicle behind are large vehicles compared to when there is a large vehicle on one side.
[0058] After determining the first safe distance from the vehicle in front and the second safe distance from the vehicle behind, the vehicle continuously assesses whether the first distance between itself and the vehicle in front is less than or equal to the first safe distance, and whether the second distance between itself and the vehicle behind is less than or equal to the second safe distance. Based on the number of "yes" results, the target risk level is determined.
[0059] S204, Output the target risk level and the corresponding prompt information.
[0060] In this embodiment of the disclosure, after obtaining the target risk level, the output module of the control vehicle outputs corresponding prompt information based on the target risk level.
[0061] For example, suppose the target risk level includes low risk, medium risk, and high risk. Then, as the risk level increases, the intensity of the output prompt message increases.
[0062] For example, when the risk level is low, the vehicle's in-car speakers can be controlled to play voice prompts, and the corresponding prompt icons on the vehicle's dashboard can be highlighted.
[0063] For example, when the risk level is medium risk, the vehicle's in-vehicle speakers can be controlled to play voice prompts, the vehicle's buzzer can be controlled to sound at a low volume, and the vehicle's steering wheel can be controlled to vibrate at a low amplitude and frequency.
[0064] For example, when the risk level is high, the system can control the vehicle's in-vehicle speakers to play voice prompts, control the vehicle's buzzer to sound a sharp beep, control the edges of the vehicle's infotainment display, LCD instrument panel, and other in-vehicle displays to flash red animation effects, and control the vehicle's steering wheel to vibrate at high frequency and with large amplitude.
[0065] This disclosure enables accurate identification of high-risk scenarios where a vehicle is surrounded by large vehicles. When a scenario where a vehicle is surrounded by large vehicles is identified, it determines whether the distances between the vehicle in front and behind are less than the safe distance, thereby determining the target risk level. Based on the target risk level, corresponding prompts are output to alert the driver of the potential risk of being surrounded by large vehicles. This disclosure fills the gap in providing accurate and timely alerts regarding the risk of being surrounded by large vehicles, thereby effectively reducing driving risks and improving driving safety.
[0066] In some embodiments of this disclosure, optionally, the target risk level is determined based on a comparison between a first distance and a first safe distance, and a comparison between a second distance and a second safe distance, including: If the first distance is less than or equal to the first safety distance, or the second distance is less than or equal to the second safety distance, the target risk level is determined to be medium risk level; If the first distance is less than or equal to the first safety distance and the second distance is less than or equal to the second safety distance, the target risk level is determined to be high risk level.
[0067] In this embodiment of the disclosure, the target risk level, exemplarily, includes no risk, low risk, medium risk, and high risk. The higher the target risk level, the higher the probability of a vehicle accident and the greater the potential severity of the accident.
[0068] In cases where there are no other large vehicles traveling in the same lane as the current vehicle, the target risk level is no risk. This means the vehicle faces no risk of being sandwiched by large vehicles or colliding with one.
[0069] When a large vehicle is present only on one side of the vehicle's lane, either in front of or behind it, or when both the vehicle in front and behind are large vehicles, but the first distance between the vehicle and the vehicle in front is greater than the first safe distance, and the second distance between the vehicle and the vehicle behind is greater than the second safe distance, the target risk level is determined to be low risk. In this case, since there is only a large vehicle on one side of the vehicle and a safe distance is maintained between the vehicle and the large vehicle, the probability of a collision with the large vehicle is relatively low.
[0070] If the first distance between the vehicle and the vehicle in front is less than or equal to the first safe distance for the vehicle in front, but the second distance between the vehicle and the vehicle behind is greater than the second safe distance, then the target risk level is determined to be medium risk. This indicates that the vehicle is sandwiched between larger vehicles, and while it is relatively close to the vehicle in front, it maintains a safe distance from the vehicle behind. In this situation, there is a risk of the vehicle colliding with the vehicle in front, but the probability of colliding with both vehicles simultaneously is relatively low.
[0071] If the first distance between the vehicle and the vehicle in front is greater than the first safe distance for the vehicle in front, but the second distance between the vehicle and the vehicle behind is less than or equal to the second safe distance, then the target risk level is also determined to be medium risk. This indicates that the vehicle is sandwiched between larger vehicles, maintaining a safe distance from the vehicle in front, but the vehicle behind is relatively close to the current vehicle. In this situation, the vehicle faces the risk of being rear-ended, but the probability of being pushed by the following vehicle into the vehicle in front, and being squeezed by both vehicles, is relatively small.
[0072] If the first distance between the vehicle and the vehicle in front is less than or equal to the first safe distance for the vehicle in front, and simultaneously the second distance between the vehicle and the vehicle behind is less than or equal to the second safe distance, then the target risk level is determined to be high risk. This indicates that the vehicle is sandwiched between large vehicles, and the distances between the vehicle and both the vehicles in front and behind are less than the safe distances. In this situation, if the vehicle rear-ends the vehicle in front, or is rear-ended by the vehicle behind, there is a possibility that the vehicle will simultaneously collide with both vehicles and be crushed by them. The vehicle faces a significant safety risk in this situation.
[0073] The embodiments of this disclosure can accurately identify potential risk scenarios of being sandwiched by large vehicles, determine the target risk level based on the risk scenario, and output corresponding prompt information based on the target risk level, thereby achieving accurate prompts for the risk of being sandwiched and reducing the risk of vehicles being squeezed by large vehicles in front and behind.
[0074] In some embodiments of this disclosure, optionally, determining the target safety distance corresponding to the target vehicle based on the vehicle's second driving speed and first driving speed includes: The relative speed between the vehicle and the target vehicle is obtained based on the difference between the second and first driving speeds. A first risk coefficient is determined based on the ratio of the target risk constant to the relative speed. A second risk coefficient is determined based on the ratio of the second driving speed to the reference speed. A third risk coefficient corresponding to the vehicle's environmental information is determined based on the mapping relationship between environmental information and risk coefficients. Environmental information includes at least one of weather information and road information. The target safe distance is determined based on the product of the initial safe distance, the first risk coefficient, the second risk coefficient, and the third risk coefficient.
[0075] In this embodiment of the disclosure, the target safety distance can be determined by the following formula (1): D safe =D base ×[(1+K v_ego ×(V ego / V ref ))]×[(1+K v_rel / (|V rel |+e))]×(1+K weather )×(1+K road )×(1+K type (1) Among them, D safe D is the target safe distance. base As the initial safe distance, K v_ego V represents the vehicle's speed risk factor. ego V is the second driving speed. ref For reference speed, K v_rel Let V be the target risk constant. rel Let K be the relative velocity, e be a preset constant, and K be the relative velocity. weather K is the weather impact coefficient. road K is the road impact coefficient. type This represents the influence coefficient of vehicle type.
[0076] Among them, D base The initial safe distance can be calibrated, for example, based on the vehicle model, vehicle driving performance, and vehicle sensor performance. For example, the initial safe distance ranges from 20m to 70m. For example, the initial safe distance is 50m.
[0077] Relative velocity |V rel |=|VV ego |, where V is the first speed of the target vehicle, and the absolute value is used in the calculation.
[0078] e is a preset constant used to prevent the denominator from being zero when |Vrel|=0. For example, e=1.
[0079] K v_ego The vehicle speed risk factor can be determined based on information such as the second driving speed and vehicle model. For example, K... v_ego It can take values of 0.3, 0.5, or 0.8. For example, K... v_ego It is positively correlated with the second driving speed.
[0080] The third risk factor is (1+K) weather )×(1+K road ), where K weather For example, K is the weather impact coefficient, which is used when the weather is clear and visibility is good. weatherThe value is 0. In weather conditions such as light rain or fog, which have minimal impact on driving, K... weather It is 0.2. In heavy rain, dense fog, or snow, K... weather It is 0.5.
[0081] K road For example, K is the road impact coefficient. For roads with relatively good conditions, such as highways, national roads, and urban roads, K... road The value is 0. For mountain roads, winding roads, and construction sections, K... road The value is 0.1. For closed road sections such as tunnels and bridges, K... road It is 0.15.
[0082] K type This is the vehicle type influence coefficient, which is related to the vehicle type of the target vehicle. For example, K... type This is an optional parameter, and its initial value is 0. For example, K... type= The increase is due to the increase in the length, weight, and height of the target vehicle.
[0083] For example, taking a specific scenario as an example, scenario one corresponds to a highway condition, where the distance between the vehicle and the target vehicle is rapidly approaching. Let V... ego =120km / h, |V rel If |=20km / h, then the target safety distance D safe =50×(1+0.5×120 / 100)×(1+5 / (20+e))≈50×1.6×1.249≈99m.
[0084] Scenario 2 corresponds to a high-speed, constant-speed following vehicle condition. Let V... ego =120km / h, |V rel If |=0km / h, then the target safety distance D safe =50×(1+0.5×120 / 100)×(1+5 / (0+e))≈50×1.6×51≈408m. Because drivers are prone to fatigue when following other vehicles at high speeds, increasing the safe following distance is beneficial for improving driving safety. For example, speed sensitivity can be reduced by adjusting the vehicle's speed risk coefficient.
[0085] Scenario 3 corresponds to a situation where the vehicle is traveling at low speed, slowly approaching the target vehicle. Let V... ego =30km / h, |Vrel|=5km / h. Therefore, the target safety distance D safe =50×(1+0.5×30 / 100)×(1+5 / (5+e))≈50×1.15×1.996≈105m.
[0086] The embodiments disclosed herein can dynamically calculate the safe distance between the vehicle and the target vehicle based on the actual driving environment during vehicle operation. This helps the driver maintain a suitable safe distance in various scenarios such as following a large vehicle, being followed by a large vehicle, or being sandwiched by a large vehicle, thereby improving vehicle driving safety.
[0087] In some embodiments of this disclosure, optionally, Figure 3 One of the flowcharts illustrating the determination of the vehicle type of a target vehicle according to some embodiments of this disclosure is shown, such as... Figure 3 As shown, the vehicle type of the target vehicle traveling in the same lane as the vehicle is obtained, including: S302, Obtain the detection information of the target vehicle.
[0088] The detection information includes image information, point cloud information, and reflection information.
[0089] For example, the vehicle includes an image sensor, such as an onboard camera. The image sensor captures images of the environment in front of, behind, and around the vehicle to obtain image information.
[0090] For example, the vehicle includes a lidar sensor. The lidar sensor can collect point cloud information of obstacles and other vehicles within a certain range near the vehicle.
[0091] For example, the vehicle includes a microwave radar. The microwave radar can collect information about obstacles and reflections from other vehicles within a certain range near the vehicle.
[0092] S304, input the detection information into the pre-trained detection model to obtain the vehicle type of the target vehicle; wherein, the pre-trained detection model extracts features from the detection information to obtain the feature information of the target vehicle, processes the feature information, and outputs the vehicle type.
[0093] In this embodiment of the disclosure, for example, the pre-trained detection model can be designed based on large-scale training data (including training materials such as images and videos, as well as structural data training materials such as point cloud information and reflection information), and a large-scale neural network algorithm structure is trained to learn and implement it. The final large-scale neural network algorithm structure and parameters are the pre-trained detection model.
[0094] For example, the neural network algorithm structure used to train the pre-trained detection model can be CNN, RNN, LSTM, etc., or it can be a model built with an attention network, such as transformer, BERT, GPT, Clip, etc., which are not limited herein.
[0095] For example, the pre-trained detection model includes multiple network layers or functional modules. For example, the detection information of the target vehicle is input into the pre-trained detection model. The pre-trained detection model may include modules for feature extraction. For example, before feature extraction, the input detection information may be convolved once or multiple times. Feature extraction yields the feature information of the target vehicle. The feature information can be a feature vector. After obtaining the feature information, the backbone network identifies the feature information, ultimately outputting the corresponding vehicle type.
[0096] Different types of vehicles have different appearance features, point cloud features, and reflection features. For example, the detection information includes the image information of the target vehicle. By using a pre-trained detection model to perform image recognition on the vehicle image information, the vehicle type of the target vehicle can be identified based on its appearance, such as whether the target vehicle is a large truck, construction vehicle, heavy transport vehicle, or ordinary passenger car.
[0097] For example, the detection information includes point cloud information of the target vehicle. For instance, if the target vehicle is a following vehicle, LiDAR can collect point cloud information of the front of the following vehicle. This point cloud information reflects the structural features of the front of the following vehicle. A pre-trained detection model identifies these structural features, thus determining the vehicle type based on the front structure of the target vehicle.
[0098] This embodiment of the disclosure collects real-time detection information of target vehicles in the same lane, including vehicles in front and behind, using sensors such as image sensors, lidar, or microwave radar during vehicle operation. By identifying the detection information through a pre-trained detection model, the vehicle type of the target vehicle can be accurately identified, thereby achieving accurate identification of large vehicle encirclement scenarios.
[0099] In some embodiments of this disclosure, optionally, Figure 4 A second flowchart illustrating the determination of the vehicle type of a target vehicle according to some embodiments of this disclosure is shown, such as... Figure 4 As shown, the vehicle type of the target vehicle traveling in the same lane as the vehicle is obtained, including: S402, obtain the vehicle dimensions of the target vehicle.
[0100] In this disclosure embodiment, by way of example, vehicle dimensions include at least one of vehicle length, wheelbase, and vehicle height.
[0101] For example, image information of a target vehicle can be acquired through the vehicle's image sensor, and the vehicle size of the target vehicle can be estimated by performing image recognition on the image information.
[0102] S404, based on the mapping relationship between vehicle size and vehicle type, determines the vehicle type of the target vehicle.
[0103] In this embodiment of the disclosure, for example, the vehicle's driver assistance system pre-stores a mapping relationship between different vehicle sizes and different vehicle types. After obtaining the vehicle size of the target vehicle through an image sensor or other means, the vehicle type that matches the target vehicle's size can be queried based on this mapping relationship.
[0104] For example, if the vehicle size is the vehicle length, the vehicle type of a vehicle with a length greater than 6 meters can be identified as the target type.
[0105] For example, if the vehicle size is the vehicle wheelbase, the vehicle type of a vehicle with a wheelbase greater than 3.5 meters can be identified as the target type.
[0106] After determining the vehicle type of the target vehicle, the target safe distance between the vehicle and the target vehicle is dynamically determined based on the vehicle type, the current vehicle's speed, the target vehicle's speed, and environmental factors such as weather and road conditions. When the real-time distance between the current vehicle and the target vehicle is less than or equal to the target safe distance, the corresponding prompt information is output.
[0107] This embodiment of the disclosure obtains the size information of other target vehicles in the same lane as the vehicle's travel path, determines the vehicle type based on the size information, and thus identifies whether the target vehicle is a large vehicle. Based on the identification result, the safe distance between the vehicle and the target vehicle is dynamically adjusted, thereby effectively achieving anti-pinch warning and improving vehicle driving safety.
[0108] In some embodiments of this disclosure, optionally, Figure 5 Flowcharts of vehicle anti-pinch intervention methods according to some embodiments of this disclosure are shown, such as Figure 5 As shown, the method includes: S502, obtain road information.
[0109] In this embodiment of the disclosure, the road information includes, for example, lane information of the current vehicle's lane, such as whether the current lane is the only lane, and whether the road markings are solid lines that cannot be crossed. The road information also includes, for example, lane information of adjacent lanes of the current vehicle's lane, such as whether there are vehicles in adjacent lanes that may interfere with lane changes, and whether there are rapidly approaching vehicles from behind in adjacent lanes.
[0110] By acquiring road information, it is possible to accurately determine whether a vehicle has safe conditions for changing lanes.
[0111] S504, in response to the driver's confirmation input of the prompt information, generates a lane change route based on road information.
[0112] For example, when the distance between the vehicle and the target vehicle is detected to be less than the target safe distance, especially when the target vehicle includes the vehicle in front and the vehicle behind, i.e. the current vehicle is in a state of being surrounded by large vehicles, the vehicle outputs corresponding prompt information to the driver through the in-vehicle infotainment system, speakers or dashboard, etc.
[0113] For example, when both the vehicle in front of and behind the current vehicle are large vehicles, and the first distance between the current vehicle and the vehicle in front is less than the first safe distance, while the second distance between the current vehicle and the vehicle behind is less than the second safe distance, the target risk level is determined to be high risk level, and the corresponding prompt information for high risk level is output.
[0114] Simultaneously, based on the collected road information, the vehicle determines whether it is able to change lanes to an adjacent lane to escape being sandwiched by a large vehicle. If, after outputting a prompt message, it is determined that the conditions for changing lanes to an adjacent lane are met, an additional prompt message can be output to remind the driver whether to perform the automatic lane change operation.
[0115] For example, when outputting prompts, an additional audio message can be output, such as "A risk of being sandwiched by a large vehicle has been detected. It is currently safe to change lanes. Would you like to execute the automatic lane change operation?", can be played as a voice reminder.
[0116] At this point, the driver can make a confirmation input. For example, the driver can make a confirmation input via voice, such as saying "Confirm lane change operation". The vehicle will then automatically generate a lane change path to safely change lanes to the adjacent lane based on the detected road information.
[0117] For example, the vehicle driver can also perform the above confirmation input via buttons on the multi-function steering wheel, vehicle central control buttons, driver assistance levers, or by touching the central control screen. This disclosure does not limit the specific input method.
[0118] S506, based on lane change path control, controls vehicles to change lanes.
[0119] In this embodiment of the disclosure, after the lane change path is calculated, the vehicle's driver assistance function intervenes and automatically controls the vehicle to change lanes to the adjacent lane according to the lane change path.
[0120] For example, Figure 6 A flowchart illustrating some embodiments of the anti-pinch control method of this disclosure is shown, such as... Figure 6 As shown, the vehicle's anti-pinch control system includes a forward sensor group, a rearward sensor group, a setting panel, a logic judgment unit, an intelligent driving module, an alert module, and a vehicle steering control module.
[0121] The system obtains user settings through a settings panel and determines whether to enable the anti-pinch function based on these settings. The settings panel provides the user settings to the logic judgment unit. When the anti-pinch function is enabled, the logic judgment unit obtains the front vehicle data from the forward sensor group and the rear vehicle data from the rear sensor group. Combining the front and rear vehicle data, it performs vehicle type determination, dynamic threshold generation, and state logic calculations.
[0122] The above steps can determine whether there is a risk of being surrounded. When a risk of being surrounded is determined, an alert is triggered to the alert module, causing the alert module to output a prompt message.
[0123] After outputting the prompt message, the intelligent driving module performs assisted lane change judgment based on road information to determine whether the conditions for a safe lane change are met, and submits the result to the logic judgment unit. At this time, the logic judgment unit outputs a lane change reminder to the user through the reminder module.
[0124] After the logic judgment unit obtains the user's lane-changing intention, it feeds back the user's lane-changing intention to the intelligent driving module. At this time, the intelligent driving module outputs the lane-changing path through the vehicle steering control module. The vehicle steering control module then controls the vehicle to change lanes according to the lane-changing path.
[0125] The embodiments disclosed herein can dynamically set a safe distance threshold based on the vehicle type of the vehicles in front and behind, thereby providing risk warnings for the potential risk of being sandwiched by large vehicles. In this case, the driver assistance function can intervene in lane changes, thereby avoiding misoperation caused by driver tension due to the risk of being sandwiched, and further improving vehicle driving safety.
[0126] In some embodiments of this disclosure, a vehicle information display device is provided. Figure 7 Structural block diagrams of vehicle information display devices according to some embodiments of the present disclosure are shown, such as... Figure 7 As shown, the prompting device 700 includes: an acquisition module 702, a determination module 704, and an output module 706.
[0127] The acquisition module 702 is used to acquire the vehicle type and first speed of a target vehicle traveling in the same lane as the vehicle during the vehicle's operation. The determination module 704 is used to determine the target safety distance corresponding to the target vehicle based on the vehicle's second driving speed and first driving speed, provided that the vehicle type matches the target type. The output module 706 is used to output corresponding prompt information based on the comparison results between the target safety distance and the vehicle and the target vehicle.
[0128] This embodiment of the disclosure collects real-time data on other vehicles traveling in the same lane as the vehicle and identifies the vehicle type of these vehicles. When a target type of vehicle, such as a large vehicle, is identified in front or behind in the same lane, the target safety distance of the vehicle collision warning system is dynamically adjusted based on the first speed of the large vehicle and the second speed of the current vehicle. By dynamically adjusting the target safety distance, the vehicle collision warning system can adapt to various road conditions, reduce false alarms or missed alarms, and improve vehicle driving safety.
[0129] In some embodiments of this disclosure, optionally, the determining module is further configured to determine the target risk level based on a comparison result of a first distance and a first safe distance, and a comparison result of a second distance and a second safe distance, when the target vehicle includes a preceding vehicle and a following vehicle; wherein, the preceding vehicle is the vehicle located in front of the vehicle in the direction of travel, and the following vehicle is the vehicle located behind the vehicle in the direction of travel; the first distance is the distance between the vehicle and the preceding vehicle, and the first safe distance is the target safe distance corresponding to the preceding vehicle; the second distance is the distance between the vehicle and the following vehicle, and the second safe distance is the target safe distance corresponding to the following vehicle; The output module is also used to output the corresponding prompt information for the target risk level.
[0130] This disclosure enables accurate identification of high-risk scenarios where a vehicle is surrounded by large vehicles. When a scenario where a vehicle is surrounded by large vehicles is identified, it determines whether the distances between the vehicle in front and behind are less than the safe distance, thereby determining the target risk level. Based on the target risk level, corresponding prompts are output to alert the driver of the potential risk of being surrounded by large vehicles. This disclosure fills the gap in providing accurate and timely alerts regarding the risk of being surrounded by large vehicles, thereby effectively reducing driving risks and improving driving safety.
[0131] In some embodiments of this disclosure, optionally, the determining module is further configured to determine the target risk level as medium risk level when the first distance is less than or equal to the first safety distance, or the second distance is less than or equal to the second safety distance; or, when the first distance is less than or equal to the first safety distance and the second distance is less than or equal to the second safety distance, determine the target risk level as high risk level.
[0132] The embodiments of this disclosure can accurately identify potential risk scenarios of being sandwiched by large vehicles, determine the target risk level based on the risk scenario, and output corresponding prompt information based on the target risk level, thereby achieving accurate prompts for the risk of being sandwiched and reducing the risk of vehicles being squeezed by large vehicles in front and behind.
[0133] In some embodiments of this disclosure, optionally, the determining module is further configured to: obtain the relative speed between the vehicle and the target vehicle based on the difference between the second driving speed and the first driving speed; determine a first risk coefficient based on the ratio of the target risk constant to the relative speed; determine a second risk coefficient based on the ratio of the second driving speed to the reference speed; determine a third risk coefficient corresponding to the vehicle's environmental information based on the mapping relationship between environmental information and risk coefficients; the environmental information includes at least one of weather information and road information; and determine a target safe distance based on the product of the initial safe distance, the first risk coefficient, the second risk coefficient, and the third risk coefficient.
[0134] The embodiments disclosed herein can dynamically calculate the safe distance between the vehicle and the target vehicle based on the actual driving environment during vehicle operation. This helps the driver maintain a suitable safe distance in various scenarios such as following a large vehicle, being followed by a large vehicle, or being sandwiched by a large vehicle, thereby improving vehicle driving safety.
[0135] In some embodiments of this disclosure, optionally, the acquisition module is further configured to acquire detection information of the target vehicle; the detection information includes image information, point cloud information, and reflection information; The determination module is also used to input the detection information into the pre-trained detection model to obtain the vehicle type of the target vehicle; wherein, the pre-trained detection model extracts features from the detection information to obtain the feature information of the target vehicle, processes the feature information, and outputs the vehicle type.
[0136] This embodiment of the disclosure collects real-time detection information of target vehicles in the same lane, including vehicles in front and behind, using sensors such as image sensors, lidar, or microwave radar during vehicle operation. By identifying the detection information through a pre-trained detection model, the vehicle type of the target vehicle can be accurately identified, thereby achieving accurate identification of large vehicle encirclement scenarios.
[0137] In some embodiments of this disclosure, optionally, the acquisition module is also used to acquire the vehicle dimensions of the target vehicle; The determination module is also used to determine the vehicle type of the target vehicle based on the mapping relationship between vehicle size and vehicle type.
[0138] This embodiment of the disclosure obtains the size information of other target vehicles in the same lane as the vehicle's travel path, determines the vehicle type based on the size information, and thus identifies whether the target vehicle is a large vehicle. Based on the identification result, the safe distance between the vehicle and the target vehicle is dynamically adjusted, thereby effectively achieving anti-pinch warning and improving vehicle driving safety.
[0139] Optionally, in some embodiments of this disclosure, the acquisition module is also configured to acquire road information; The information prompting device also includes a generation module, which generates a lane-changing route based on road information in response to the driver's confirmation input of the prompt information. The control module is used to control the vehicle to change lanes based on the lane change path.
[0140] The embodiments disclosed herein can dynamically set a safe distance threshold based on the vehicle type of the vehicles in front and behind, thereby providing risk warnings for the potential risk of being sandwiched by large vehicles. In this case, the driver assistance function can intervene in lane changes, thereby avoiding misoperation caused by driver tension due to the risk of being sandwiched, and further improving vehicle driving safety.
[0141] In some embodiments of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the vehicle information prompting method provided in any of the above embodiments.
[0142] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.
[0143] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.
[0144] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.
[0145] In some embodiments of this disclosure, a vehicle is provided, comprising: Memory; Processor; and Computer programs; The computer program is stored in a memory and configured to be executed by a processor to implement the steps of the vehicle information prompting method provided in any of the above embodiments.
[0146] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0147] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for displaying vehicle information, characterized in that, The method includes: During the vehicle's operation, the vehicle type and first speed of the target vehicle traveling in the same lane as the vehicle are obtained. If the vehicle type matches the target type, the target safety distance corresponding to the target vehicle is determined based on the vehicle's second driving speed and first driving speed. Based on the target safe distance and the distance comparison result between the vehicle and the target vehicle, the corresponding prompt information is output.
2. The method according to claim 1, characterized in that, Based on the target safety distance and the distance comparison result between the vehicle and the target vehicle, the corresponding prompt information includes: When the target vehicle includes a preceding vehicle and a following vehicle, the target risk level is determined based on a comparison of a first distance and a first safe distance, and a comparison of a second distance and a second safe distance; wherein, the preceding vehicle is the vehicle located in front of the vehicle in the direction of travel of the vehicle, and the following vehicle is the vehicle located behind the vehicle in the direction of travel of the vehicle; the first distance is the distance between the vehicle and the preceding vehicle, and the first safe distance is the target safe distance corresponding to the preceding vehicle; the second distance is the distance between the vehicle and the following vehicle, and the second safe distance is the target safe distance corresponding to the following vehicle; Output the corresponding prompt information for the target risk level.
3. The method according to claim 2, characterized in that, The determination of the target risk level based on the comparison results of the first distance and the first safe distance, and the comparison results of the second distance and the second safe distance, includes: If the first distance is less than or equal to the first safe distance, or the second distance is less than or equal to the second safe distance, the target risk level is determined to be a medium risk level. If the first distance is less than or equal to the first safe distance, and the second distance is less than or equal to the second safe distance, the target risk level is determined to be a high-risk level.
4. The method according to claim 1, characterized in that, Determining the target safe distance corresponding to the target vehicle based on the second driving speed and the first driving speed of the vehicle includes: The relative speed between the vehicle and the target vehicle is obtained based on the difference between the second driving speed and the first driving speed. The first risk coefficient is determined based on the ratio of the target risk constant to the relative velocity. The second risk factor is determined based on the ratio of the second driving speed to the reference speed; Based on the mapping relationship between environmental information and risk coefficient, a third risk coefficient corresponding to the environmental information of the vehicle is determined; the environmental information includes at least one of weather information and road information. The target safety distance is determined based on the product of the initial safety distance, the first risk coefficient, the second risk coefficient, and the third risk coefficient.
5. The method according to any one of claims 1 to 4, characterized in that, The step of obtaining the vehicle type of the target vehicle traveling in the same lane as the vehicle includes: Acquire the detection information of the target vehicle; the detection information includes image information, point cloud information, and reflection information; The detection information is input into a pre-trained detection model to obtain the vehicle type of the target vehicle; wherein, the pre-trained detection model extracts features from the detection information to obtain feature information of the target vehicle, processes the feature information, and outputs the vehicle type.
6. The method according to any one of claims 1 to 4, characterized in that, The step of obtaining the vehicle type of the target vehicle traveling in the same lane as the vehicle includes: Obtain the vehicle dimensions of the target vehicle; The vehicle type of the target vehicle is determined based on the mapping relationship between vehicle size and vehicle type.
7. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Obtain road information; In response to the driver's confirmation of the prompt information, a lane change path is generated based on the road information; The vehicle changes lanes based on the lane change path.
8. A vehicle information display device, characterized in that, The device includes: The acquisition module is used to acquire the vehicle type and the first speed of a target vehicle traveling in the same lane as the vehicle during the vehicle's operation. The determination module is used to determine the target safety distance corresponding to the target vehicle based on the second driving speed and the first driving speed of the vehicle when the vehicle type matches the target type. The output module is used to output corresponding prompt information based on the target safety distance and the distance comparison result between the vehicle and the target vehicle.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-7.
10. A vehicle, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-7.