Patent Publication Number: US-2023159031-A1

Title: Safe following distance estimation system and estimation method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of Taiwan application serial no. 110143172, filed on Nov. 19, 2021. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
     BACKGROUND 
     Technical Field 
     The disclosure relates to a safe following distance estimation system and an estimation method thereof, and in particular to a safe following distance estimation system used in an autonomous vehicle and an estimation method thereof. 
     Description of Related Art 
     In order to avoid collisions caused by emergency braking, one of the most effective strategies is to maintain a proper safe following distance between an autonomous vehicle and surrounding vehicles and objects during driving. Regarding the safety verification and monitoring of autonomous vehicles, the Electrical and Electronics Engineers Standards Association (IEEE-SA) published a safety specification for autonomous vehicles, IEEE P2846 (Formal Model for Safety Considerations in Automated Vehicle Decision Making). IEEE P2846 includes a responsibility-sensitive safety (RSS) model. The RSS model defines the safety status of autonomous vehicles and provides measurable parameters. The RSS model includes common rules of keeping a certain safe following distance, that is, a distance to avoid collisions under the worst conditions, from the surrounding vehicles in different scenarios, so as to avoid traffic accidents. 
     However, maintaining an appropriate safe following distance between an autonomous vehicle and surrounding vehicles with different dynamic performance in different traffic environments is a challenge for the development of autonomous vehicle technology. For example, a large truck loaded with cargo may have a larger deceleration, so an autonomous vehicle behind the large truck needs to pull a longer safe following distance. When an autonomous vehicle is driving on a pavement on which there is water or snow, the friction coefficient between wheels and the pavement is lower than that in a condition of a dry pavement, resulting in a significant reduction in the braking ability of the autonomous vehicle. Therefore, the safe following distance needs to be increased. On the other hand, through a proper safe following distance, an excessive distance between a leading vehicle and a following vehicle may be avoided, and the efficiency of a traffic network may be maintained. 
     SUMMARY 
     The disclosure provides a safe following distance estimation system and an estimation method thereof, which calculate a safe following distance between an adjacent vehicle and an autonomous vehicle through identifying a dynamic specification of the adjacent vehicle and estimating friction parameters between wheels of the adjacent vehicle and the autonomous vehicle and the pavement. 
     The embodiment of the disclosure provides a safe following distance estimation system, adapted for an autonomous vehicle. The safe following distance estimation system includes but is not limited to include a sensor and a processor. The sensor senses an adjacent vehicle to generate first sensing data, and senses the autonomous vehicle to generate second sensing data, and the adjacent vehicle is adjacent to the autonomous vehicle. The processor estimates a first friction parameter between wheels of the adjacent vehicle and a pavement according to pavement material data, estimates a second friction parameter between wheels of the autonomous vehicle and the pavement according to the second sensing data, and calculates a safe following distance between the autonomous vehicle and the adjacent vehicle according to the first sensing data, the second sensing data, the first friction parameter, and the second friction parameter. 
     The embodiment of the disclosure provides a safe following distance estimation method, adapted for an autonomous vehicle. The safe following distance estimation method includes the following. An adjacent vehicle is sensed to generate first sensing data, and the autonomous vehicle is sensed to generate second sensing data, and the adjacent vehicle is adjacent to the autonomous vehicle. A first friction parameter between wheels of the adjacent vehicle and a pavement is estimated according to pavement material data. A second friction parameter between wheels of the autonomous vehicle and the pavement is estimated according to the second sensing data. A safe following distance between the autonomous vehicle and the adjacent vehicle is calculated according to the first sensing data, the second sensing data, the first friction parameter, and the second friction parameter. 
     To provide a further understanding of the above features and advantages of the disclosure, embodiments accompanied with drawings are described below in details. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a block diagram of a safe following distance estimation system according to an embodiment of the disclosure. 
         FIG.  2    illustrates a flow chart of a safe following distance estimation method according to an embodiment of the disclosure. 
         FIG.  3    illustrates a flow chart of calculating a safe following distance and performing a safety verification according to an embodiment of the disclosure. 
         FIG.  4    illustrates a flow chart of generating a dynamic specification according to an embodiment of the disclosure. 
         FIG.  5    illustrates a top view of a distance between vehicles according to an embodiment of the disclosure. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     The term “coupling (or connection)” used in the full text of the specification of this application (including the claims) may refer to any direct or indirect connection method. For example, if it is described in the text that a first device is coupled (or connected) to a second device, it should be interpreted as that the first device may be directly connected to the second device, or the first device may be indirectly connected to the second device through other devices or a kind of connection method. In addition, wherever possible, elements/components/steps with the same reference numeral in the drawings and embodiments represent the same or similar parts. Elements/components/steps with the same reference numeral or same term in different embodiments may be referred to for related descriptions. 
       FIG.  1    illustrates a block diagram of a safe following distance estimation system according to an embodiment of the disclosure.  FIG.  2    illustrates a flow chart of a safe following distance estimation method according to an embodiment of the disclosure. Referring to  FIGS.  1  and  2   , a safe following distance estimation system  10  includes but is not limited to include a sensor  110  and a processor  120 , and the processor  120  is coupled to the sensor  110 . In an embodiment of the present disclosure, the safe following distance estimation system  10  is adapted to an autonomous vehicle, and the safe following distance estimation system  10  may receive a plurality of sensing data and a high-precision map through the sensor  110  of the autonomous vehicle and a communication system (not shown), and generates a safe following distance DMIN between the autonomous vehicle and an adjacent vehicle through a responsibility-sensitive safety model (RSS model) according to the plurality of sensing data and the high-precision map. The safe following distance DMIN allows the processor  120  to further determine whether it is necessary to perform auxiliary control on the autonomous vehicle to avoid accidents, and a top view may be displayed through a display device (not shown) on the autonomous vehicle for the driver&#39;s reference. 
     In an embodiment, the sensor  110  may include a camera, LiDAR, radar, an accelerometer, a gyroscope, a weather sensor, a wheel speedometer, a thermometer, etc., and the number and type thereof is not limited. The processor  120  includes, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a programmable controller, a programmable logic device (PLD), or other similar devices, or a combination of these devices, and is not limited thereto. 
     In step S 210 , the sensor  110  senses an adjacent vehicle to generate first sensing data S 1 , and senses the autonomous vehicle to generate second sensing data S 2 , and the adjacent vehicle is adjacent to the autonomous vehicle. The first sensing data S 1  includes but is not limited to include a longitudinal velocity V f , a lateral velocity, image data, and LiDAR data of the adjacent vehicle, and the second sensing data S 2  includes but is not limited to include a longitudinal velocity V r , a lateral velocity, a wheel velocity, a wheel deflection angle, and a yaw rate of the autonomous vehicle and a pavement inclination angle degree. 
     In step S 220 , pavement material data PM is generated through the processor  120  looking up the high-precision map or the sensor  110  sensing the pavement, and a first friction coefficient μ1 between the adjacent vehicle and the pavement is estimated according to the pavement material data PM. The high-precision map has a very high accuracy of centimeter level, and includes complex information of the pavement, such as slope, curvature, road boundary, pavement texture, traffic signs, etc. The processor  120  may receive the high-precision map through the communication system/telecommunications network, and look up the high-precision map to generate the pavement material data PM of the pavement where the adjacent vehicle is currently located, for example, asphalt pavement, cement pavement or soil pavement, etc. Next, the local weather is obtained through the communication system/telecommunications network or the temperature outside the car and the material covered on the pavement are sensed by the sensor  110 , so as to further update the pavement material data PM, such as asphalt pavement (dry), asphalt pavement (wet), ice surface, snow pavement, etc. On the other hand, it is also possible to sense the pavement by the sensor  110  and compare with the database by the processor  120  to generate the pavement material data PM, and the disclosure is not limited thereto. After the pavement material data PM is generated, the processor  120  may input the pavement material data PM into a lookup table (Table 1) to generate the first friction coefficient μ1 between the adjacent vehicle and the pavement. The lookup table includes the correspondence between the pavement material data PM and the first friction coefficient μ1. It must be noted that the first friction coefficient μ1 is the maximum value of the friction coefficient corresponding to the pavement material data PM. For example, when the pavement material is asphalt pavement (dry), the corresponding maximum friction coefficient is 0.8 to 0.9. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Pavement material data PM 
                 First friction coefficient μ1 
               
               
                   
                   
               
             
            
               
                   
                 Asphalt pavement (dry) 
                 0.8 to 0.9 
               
               
                   
                 Asphalt pavement (wet) 
                 0.5 to 0.7 
               
               
                   
                 Snow pavement 
                 0.2 
               
               
                   
                 Ice 
                 0.1 
               
               
                   
                   
               
            
           
         
       
     
     In step S 230 , the processor  120  may estimate a second friction coefficient μ2 and a rolling friction coefficient μr between the autonomous vehicle and the pavement according to the second sensing data S 2 . In an embodiment, the processor  120  generates the pavement inclination angle and a planned path curve through looking up the high-precision map or the sensor  110  sensing the pavement. Next, the second friction coefficient μ2 between the autonomous vehicle and the pavement is calculated through the longitudinal velocity V r , the wheel velocity, the wheel deflection angle, the yaw rate, the pavement inclination angle, and the planned path curve of the autonomous vehicle in the second sensing data. European Patent EP3106360A1 may be referred to for the specific calculation method. After the pavement material data PM is generated, the processor  120  may input the pavement material data PM into the lookup table (Table 2) to generate the rolling friction coefficient μr between the autonomous vehicle and the pavement. The lookup table includes the correspondence between the pavement material PM and rolling friction coefficient μr. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Pavement material data PM 
                 Rolling friction coefficient μr 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Good asphalt pavement 
                 0.0068 
               
               
                   
                 Inferior asphalt pavement 
                 0.0127 
               
               
                   
                 Sand pavement 
                 0.250 
               
               
                   
                   
               
            
           
         
       
     
     Next, in step S 240 , the processor  120  calculates the safe following distance DMIN between the autonomous vehicle and the adjacent vehicle according to the first sensing data S 1 , the second sensing data S 2 , the first friction coefficient μ1, the second friction coefficient μ2, and the rolling friction coefficient μr. The details of calculating the safe following distance DMIN will be explained in detail in  FIG.  3   . 
       FIG.  3    illustrates a flow chart of calculating a safe following distance and performing a safety verification according to an embodiment of the disclosure. Referring to  FIG.  3   , in step S 310 , the sensor  110  senses the adjacent vehicle to generate the first sensing data S 1 . Next, in step S 320 , the processor  120  receives the first sensing data S 1  and identifies the first sensing data S 1  to identify the vehicle model of the adjacent vehicle, and looks up the corresponding dynamic specification DS according to the identified vehicle model. The dynamic specification DS at least includes the maximum deceleration in the adjacent vehicle specification, that is, the maximum braking deceleration of the adjacent vehicle. It is worth mentioning that since the adjacent vehicle adjacent to the autonomous vehicle may continuously change in the driving process, the first sensing data sensed by the sensor  110  also change. Therefore, the dynamic specification dynamically changes accordingly. The details of identification in step S 320  will be explained in detail in  FIG.  4   . 
     In step S 330 , the processor  120  obtains the pavement material data PM. Specifically, the pavement material data PM may be obtained through the processor  120  looking up the high-precision map or the sensor  110  sensing the pavement, and the disclosure is not limited thereto. Next, in step S 340 , the processor  120  inputs the pavement material data PM into the lookup table (see Table 1) to generate the first friction coefficient μ1 between the adjacent vehicle and the pavement. 
     In step S 350 , the sensor  110  senses the autonomous vehicle to generate the second sensing data S 2 . Next, in step S 360 , the processor  120  receives the second sensing data S 2 , and estimates the second friction coefficient μ2 and the rolling friction coefficient μr according to the second sensing data S 2 . In an embodiment, the processor  120  calculates the second friction coefficient μ2 between the autonomous vehicle and the pavement through the longitudinal velocity Vr, the wheel velocity, the wheel deflection angle, the yaw rate, and the pavement inclination angle of the autonomous vehicle in the second sensing data. The pavement material data PM are entered into the lookup table (see Table 2) to generate the rolling friction coefficient μr between the autonomous vehicle and the pavement. Step S 230  may be referred to for details, which will not be repeated herein. 
     In step S 370 , the processor  120  estimates a first maximum deceleration A 1  of the adjacent vehicle and a second minimum deceleration A 2  and a third maximum acceleration A 3  of the autonomous vehicle according to the dynamic specification DS, the first friction coefficient μ1, the second friction coefficient μ2, and the rolling friction coefficient μr. In an embodiment, the processor  120  may substitute the maximum deceleration of the adjacent vehicle in the dynamic specification DS and the first friction coefficient μ1 between the adjacent vehicle and the pavement into the lookup table (Table 3) to estimate the first maximum deceleration A 1  of the adjacent vehicle. The first maximum deceleration A 1  is the maximum deceleration of the adjacent vehicle after the first friction coefficient μ1 is considered. For example, after the adjacent vehicle driving on a snow pavement is identified, the maximum deceleration of the adjacent vehicle, 7.5 m/s 2 , may be generated. The first friction coefficient μl corresponding to the snow pavement is 0.2. After the maximum deceleration of the adjacent vehicle and the first friction coefficient μ1 are substituted into the look-up table, it may be shown that the corresponding first maximum deceleration A 1  is 1.67 m/s 2 . Table 3 is only for illustration, and the disclosure is not limited thereto. It is worth mentioning that, compared with the maximum deceleration of the adjacent vehicle in the dynamic specification DS, the first maximum deceleration A 1  further includes the influence of the pavement friction coefficient on the braking performance of the adjacent vehicle. 
     
       
         
           
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                   
                 First maximum 
               
               
                 Maximum deceleration of 
                 First friction 
                 deceleration A1 of 
               
               
                 adjacent vehicle 
                 coefficient μ1 
                 adjacent vehicle 
               
               
                   
               
             
            
               
                 7.5 m/s 2   
                 0.2 
                 1.67 m/s 2   
               
               
                   8 m/s 2   
                 0.8 
                 7.1 m/s 2   
               
               
                 8.5 m/s 2   
                 0.6 
                 5.7 m/s 2   
               
               
                   
               
            
           
         
       
     
     In an embodiment, the processor  120  may estimate the second minimum deceleration A 2  of the autonomous vehicle according to the second friction coefficient μ2, and estimate the third maximum acceleration A 3  according to the rolling friction coefficient μr. Specifically, the maximum acceleration and the minimum deceleration in the autonomous vehicle specification are known fixed values, and the processor  120  may respectively substitute the second friction coefficient μ2 and the rolling friction coefficient μr into the lookup tables (Table 4 and Table 5) to generate the second minimum deceleration A 2  corresponding to the second friction coefficient μ2 and the third maximum acceleration A 3  corresponding to the rolling friction coefficient μr of the autonomous vehicle. The second minimum deceleration A 2  is the minimum deceleration of the autonomous vehicle after the second friction coefficient μ2 is considered, and the third maximum acceleration A 3  is the maximum acceleration of the autonomous vehicle after the rolling friction coefficient μr is considered. Regarding the second minimum deceleration A 2 , for example, assuming that the second friction coefficient μ2 of the autonomous vehicle is 0.2, the second minimum deceleration A 2  of the autonomous vehicle may be obtained as 0.89 m/s 2  after checking Table 4. Table 4 is only for illustration, and the disclosure is not limited thereto. Regarding the third maximum acceleration A 3 , the processor  120  may input the pavement material data PM into the lookup table (Table 2) of the pavement material data PM and the rolling friction coefficient μr, so as to derive the rolling friction coefficient μr corresponding to the pavement material data PM. The rolling friction coefficient μr specifically refers to the friction coefficient between the autonomous vehicle during acceleration and the pavement. For example, if the autonomous vehicle is driving on a good asphalt pavement, its rolling friction coefficient μr may be 0.0068 after the table is checked. Next, the rolling friction coefficient μr of 0.0068 is substituted into Table 5, and the third maximum acceleration A 3  corresponding to the autonomous vehicle may be obtained as 4.98 m/s 2 , after the table is checked. Table 5 is only for illustration, and the disclosure is not limited thereto. It is worth mentioning that, compared with the minimum deceleration and the maximum acceleration in the autonomous vehicle specification, the second minimum deceleration A 2  and the third maximum acceleration A 3  further include the influence of the pavement friction coefficient and the rolling friction coefficient on the braking and acceleration performance of the autonomous vehicle. 
     
       
         
           
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Second friction coefficient μ2 
                 Second minimum deceleration A2 
               
               
                   
               
             
            
               
                 0.2 
                 0.89 m/s 2   
               
               
                 0.8 
                 3.56 m/s 2   
               
               
                 0.6 
                 2.67 m/s 2   
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                   
                 TABLE 5 
               
               
                   
                   
               
               
                   
                 Rolling friction coefficient μr 
                 Third maximum acceleration A3 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 0.0068 
                 4.98 m/s 2   
               
               
                   
                 0.0127 
                 4.93 m/s 2   
               
               
                   
                 0.25 
                  2.6 m/s 2   
               
               
                   
                   
               
            
           
         
       
     
     In step S 380 , the processor  120  substitutes the first sensing data S 1 , the second sensing data S 2 , the first maximum deceleration A 1 , the second minimum deceleration A 2 , and the third maximum acceleration A 3  into the RSS model to calculate the safe following distance DMIN. Formula (1) may be referred to below. 
     
       
         
           
             
               
                 
                   
                     D 
                     ⁢ 
                     MIN 
                   
                   = 
                   
                     max 
                     ⁢ 
                     
                       { 
                       
                         0 
                         , 
                         
                           
                             
                               V 
                               r 
                             
                             ⁢ 
                             ρ 
                           
                           + 
                           
                             
                               1 
                               2 
                             
                             ⁢ 
                             A 
                             ⁢ 
                             3 
                             ⁢ 
                             
                               ρ 
                               2 
                             
                           
                           + 
                           
                             
                               
                                 ( 
                                 
                                   
                                     V 
                                     r 
                                   
                                   + 
                                   
                                     ρ 
                                     ⁢ 
                                     A 
                                     ⁢ 
                                     3 
                                   
                                 
                                 ) 
                               
                               2 
                             
                             
                               2 
                               ⁢ 
                               A 
                               ⁢ 
                               2 
                             
                           
                           - 
                           
                             
                               V 
                               f 
                               2 
                             
                             
                               2 
                               ⁢ 
                               A 
                               ⁢ 
                               1 
                             
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     In Formula (1), V r  is the longitudinal velocity of the autonomous vehicle, p is the reaction time before starting to brake, V f  is the longitudinal velocity of the adjacent vehicle, the first sensing data S 1  includes V f , and the second sensing data S 2  includes V r . 
     In step S 390 , the processor  120  may obtain a first distance D 1  between the adjacent vehicle and the autonomous vehicle according to the first sensing data S 1 . In an embodiment, LiDAR and radar in the sensor  110  may be used to measure the distance to the adjacent vehicle to derive the first distance D 1  between the adjacent vehicle and the autonomous vehicle. The processor  120  may compare the first distance D 1  with the safe following distance DMIN to determine whether the autonomous vehicle passes the safety verification. When the first distance D 1  is greater than or equal to the safe following distance DMIN, the processor  120  determines that the autonomous vehicle passes the safety verification. When the first distance D 1  is less than the safe following distance DMIN, the processor  120  determines that the autonomous vehicle has not passed the safety verification. When the processor  120  determines that the autonomous vehicle has not passed the safety verification, the processor  120  may drive the autonomous vehicle to decelerate appropriately so as to increase the first distance D 1  between the adjacent vehicle and the autonomous vehicle until the first distance D 1  is greater than the safe following distance DMIN. 
       FIG.  4    illustrates a flow chart of generating a dynamic specification according to an embodiment of the disclosure. In step S 405 , the processor  120  receives the first sensing data S 1 . Next, in step S 410 , the processor  120  extracts a plurality of features of the adjacent vehicle from the first sensing data S 1 . The first sensing data S 1  includes the image and LiDAR data of the adjacent vehicle, and the processor  120  may extract a plurality of features from the image and LiDAR data of the adjacent vehicle, such as license plate number, trademark, appearance, size, lamp shape, number of wheels, etc., and the disclosure is not limited thereto. In step S 420 , the processor  120  provides the plurality of features of the adjacent vehicle to a vehicle classifier. In an embodiment, the vehicle classifier includes a modular software that identifies the vehicle category according to the image and LiDAR features, and the vehicle classifier further includes a variety of vehicle specifications. Next, in step S 430 , the vehicle classifier determines whether the adjacent vehicle is an identifiable vehicle model according to the plurality of features mentioned above. If not, step S 440  is proceeded to, and if yes, step S 450  is proceeded to. 
     In step S 440 , since the vehicle classifier determines that the adjacent vehicle is not an identifiable vehicle model, the vehicle classifier further looks up the general specification of the vehicle category to which the adjacent vehicle belongs, such as the general specification for passenger cars, RVs, buses, and trucks, and the general specification at least includes the maximum deceleration of the adjacent vehicle. In step S 450 , since the vehicle classifier determines that the adjacent vehicle is an identifiable vehicle model, the vehicle classifier further looks up the specific specification of the vehicle category to which the adjacent vehicle belongs, for example, the specific specification of Toyota XXX or Honda YYY, and the specific specification at least includes the maximum deceleration of the adjacent vehicle. Next, in step S 460 , according to the general specification or the specific specification that is looked up, the vehicle classifier outputs a dynamic specification that includes the maximum deceleration of the adjacent vehicle. 
       FIG.  5    illustrates a top view of a distance between vehicles according to an embodiment of the disclosure. In an embodiment, the safe following distance estimation system  10  further includes a display device. The display device is used to display the top view of a distance between vehicles. The display device may be disposed on a dashboard, a windshield, a rear mirror or a mobile device, but is not limited thereto. Referring to  FIG.  5   , the display device may provide three display scenarios of the distance between vehicles. Specifically, through the sensor  110  generating the first distance D 1  between the autonomous vehicle and the adjacent vehicle, the processor  120  compares the first distance D 1  with the safe following distance DMIN. Corresponding to the three comparison results, distance indicator lines (representing the first distance DO between the autonomous vehicles and the adjacent vehicles are displayed in different colors on the display device. For example, when the first distance D 1  is greater than the safe following distance DMIN, the distance indicator line between the autonomous vehicle and the adjacent vehicle is displayed as a green line. When the first distance D 1  is equal to the safe following distance DMIN, the distance indicator line between the autonomous vehicle and the adjacent vehicle is displayed as a yellow line. When the first distance D 1  is less than the safe following distance DMIN, the distance indicator line between the autonomous vehicle and the adjacent vehicle is displayed as a red line. In this way, the driver only needs to quickly determine whether the distance to the adjacent vehicle meets the safe following distance through the color of the distance indicator line, instead of having to identify the text or numbers on the display device in detail, thereby improving user experience. 
     It must be noted that the adjacent vehicle in this text refers to a vehicle around the autonomous vehicle. The vehicle around the autonomous vehicle may be a vehicle with a longitudinal distance and/or a lateral distance from the autonomous vehicle. That is to say, the vehicle around the autonomous vehicle may be located in the front, diagonally forward, or lateral direction of the autonomous vehicle&#39;s travel route, and the number of vehicles around the autonomous vehicle may be more than one, but the disclosure is not limited thereto. 
     Since the RSS model includes a longitudinal safe following distance and a lateral safe following distance, the autonomous vehicle may consider both the longitudinal safe following distance and the lateral safe following distance. In an embodiment, if the longitudinal distance is the first distance D 1 , and the lateral distance is the second distance D 2 , the longitudinal safe following distance is, for example, a longitudinal safe following distance DMIN_V, and the lateral safe following distance is a lateral safe following distance DMIN_S. When the first distance D 1  between the autonomous vehicle and the vehicle around the autonomous vehicle is greater than the longitudinal safe following distance DMIN_V and the second distance D 2  is greater than the lateral safe following distance DMIN_S, the processor  120  determines that the longitudinal distance and the lateral distance between the autonomous vehicle and the vehicle around the autonomous vehicle have passed the safety verification, and the autonomous vehicle is in a safe state. In another scenario, when the first distance D 1  between the autonomous vehicle and the vehicle around the autonomous vehicle is less than the longitudinal safe following distance DMIN_V and the second distance D 2  is less than the lateral safe following distance DMIN_S, the processor  120  determines that the longitudinal distance and the lateral distance between the autonomous vehicle and the vehicle around the autonomous vehicle have not passed the safety verification, and the autonomous vehicle might collide with the vehicle around the autonomous vehicle. In another scenario, when the first distance D 1  between the autonomous vehicle and the vehicle around the autonomous vehicle is less than the longitudinal safe following distance DMIN_V and the second distance D 2  is greater than the lateral safe following distance DMIN_S or when the first distance D 1  between the autonomous vehicle and the vehicle around the autonomous vehicle is greater than the longitudinal safe following distance DMIN_V and the second distance D 2  is less than the lateral safe following distance DMIN_S, the processor  120  determines that one of the longitudinal distance and the lateral distance between the autonomous vehicle and the vehicle around the autonomous vehicle has not passed the safety verification, and the autonomous vehicle has no immediate risk of colliding with the vehicle around the autonomous vehicle. However, a warning message may be provided to the driver to remind the driver to avoid the situation where neither the longitudinal distance nor the lateral distance between the autonomous vehicle and the vehicle around the autonomous vehicle passes the safety verification. 
     It must be noted that the friction coefficients in the disclosure, such as the first friction coefficient μ1, the second friction coefficient μ2, and the rolling friction coefficient μr, are just examples. Other friction parameters corresponding to the characteristics of friction may be processed, for example, a first friction parameter, a second friction parameter, and a rolling friction parameter. 
     In summary, in the disclosure, through identifying the dynamic specification of the adjacent vehicle and estimating the friction coefficients between the wheels of the vehicle around the autonomous vehicle and the autonomous vehicle and the pavement, the real-time estimation accuracy of the maximum acceleration/deceleration between the autonomous vehicle and the vehicle around the autonomous vehicle may be improved, the scenarios where safe following distance can be applied may be increased, and the reliability and safety of autonomous vehicles may be improved. On the other hand, the display device is used to display different scenarios of distances between vehicles relative to the safe following distance in different colors, so as to allow the driver to quickly determine whether the distance between vehicles meets the safe following distance, thereby improving user experience. 
     Although the disclosure has been disclosed in the above by way of embodiments, the embodiments are not intended to limit the disclosure. Those with ordinary knowledge in the technical field can make various changes and modifications without departing from the spirit and scope of the disclosure. Therefore, the protection scope of the disclosure is subject to the scope of the appended claims.