Abstract:
The present invention discloses a system and method for detecting environment-induced disablement of ADAS. The system comprises a rainfall detector, a fog detector and a temperature/humidity detector respectively generating a rainfall value, a fog value, and a temperature/humidity value; a processor electrically connected with these detectors, using the rainfall value and fog value to generate a rainfall-fog value, using the fog value and temperature/humidity value to generate a snowfall value, using a fuzzy computation to process the rainfall-fog value and snowfall value to generate an output value, and emitting an alert signal if the output value exceeds a preset output value; and an automatic driver assistance device electrically connected with the processor, receiving the alert signal, and determining whether to stop automatic driving according to the alert signal. The present invention will alerts ADAS of the disablement lest ADAS execute wrong control actions.

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to a driver assistance technology, particularly to a system and method for detecting environment-induced disablement of an advanced driver assistance system. 
     Description of the Related Art 
     The advanced driver assistance system (ADAS) is one of the intelligent vehicle technologies the automobile manufacturers are enthusiastic to develop, expected to realize unmanned vehicles in future. ADAS assists drivers to drive/control vehicles, enhancing safety of drivers and traffic. ADAS normally uses image sensors, millimeter-wave radars or laser radars to detect the distance between a vehicle and a barrier so as to generate a vehicle control signal. 
     Environmental factors are likely to affect image sensors, millimeter-wave radars or laser radars of the conventional ADAS, impair the judgement of ADAS, and make ADAS fail to control the vehicle correctly. If the weather factor or road state causes a vehicle to slip, ADAS would be hard to control the direction of the vehicle. In some worse cases, a slipping vehicle may endanger the driver and others. 
     Accordingly, the present invention proposes a system and method for detecting environment-induced disablement of ADAS to overcome the abovementioned problems. 
     SUMMARY OF THE INVENTION 
     The primary objective of the present invention is to provide a system and method for detecting environment-induced disablement of an advanced driver assistance system (ADAS), which judges influences of the external environment factors on ADAS, whereby ADAS can determine the reliability of the cameras and detection radars thereof according to the judgement, wherefore ADAS is exempted from incorrect information and less likely to execute wrong control actions. 
     Another objective of the present invention is to provide a system and method for detecting environment-induced disablement of ADAS, which detects the slippage rate of a vehicle and informs ADAS of the slippage rate, whereby ADAS stops driving the vehicle while the slippage is too severe lest the slipping vehicle cause a traffic accident. 
     In order to achieve the abovementioned objectives, the present invention proposes a system for detecting environment-induced disablement of ADAS, which comprises at least one environment sensor detecting the environment and generating at least one detection result; a processor electrically connected with the environment sensor and using a fuzzy computation to process the detection result and generate an output value and emitting an alert signal if the output value exceeding a preset output value; and an automatic driver assistance device electrically connected with the processor, receiving the alert signal from the processor, and determining whether to stop automatic driving according to the alert signal. 
     The present invention also proposes a method for detecting environment-induced disablement of ADAS, which comprises steps: detecting the environment to acquire a rainfall value, a fog value and a temperature/humidity value; a processor using the rainfall value and the fog value to generate a rainfall-fog value and using the fog value and the temperature/humidity value to generate a snowfall value; the processor using a fuzzy computation to process the rainfall-fog value and the snowfall value and generate an output value; the processor determining whether the output value is greater than a preset output value; if no, the process returning to the step of detecting the environment to acquire a rainfall value, a fog value and a temperature/humidity value; if yes, the processor generating an alert signal to an automatic driver assistance device; the automatic driver assistance device determining whether to stop automatic driving according to the alert signal. 
     Below, embodiments are described in detail to make easily understood the objectives, technical contents, characteristics and accomplishments of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram schematically showing a system for detecting environment-induced disablement of ADAS according to one embodiment of the present invention; 
         FIG. 2  is a flowchart of a method for detecting environment-induced disablement of ADAS according to one embodiment of the present invention; 
         FIG. 3  is a diagram schematically showing a lookup list of output values according to one embodiment of the present invention; 
         FIG. 4  shows a table to verify validness of a camera according to one embodiment of the present invention; 
         FIG. 5  shows a table to verify validness of a laser radar according to one embodiment of the present invention; and 
         FIG. 6  shows a table to verify validness of a millimeter-wave radar according to one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Refer to  FIG. 1  a block diagram schematically showing a system for detecting environment-induced disablement of ADAS according to one embodiment of the present invention. The system  1  of the present invention comprises an environment detection device  30 , a processor  16  and an automatic driver assistance device  18 . The environment detection device  30  includes a rainfall detector  10 , a fog detector  12 , and a temperature/humidity detector  14 . The rainfall detector  10  detects the rainfall of the environment and generates a rainfall value. The fog detector  12  detects the fog of the environment and generates a fog value. The temperature/humidity detector  14  detects the temperature/humidity of the environment and generates a temperature/humidity value. The processor  16  is electrically connected with the rainfall detector  10 , the fog detector  12  and the temperature/humidity detector  14  and receives the rainfall value, the fog value and the temperature/humidity value therefrom. The processor  16  generates a rainfall-fog value according to the rainfall value and the fog value and generates a snowfall value according to the fog value and the temperature/humidity value. Then, the processor  16  uses a fuzzy computation to process the rainfall-fog value and the snowfall value to generate an output value. The processor  16  determines whether the output value is greater than a preset output value. If the output value is greater than the preset output value, the processor  16  generates an alert signal to the automatic driver assistance device  18 . In such a case, the automatic driver assistance device  18  determines whether to stop automatic driving according to the alert signal. The automatic driver assistance device  18  includes a controller  182 , a camera  184 , a millimeter-wave radar  186 , and a laser radar  188 , wherein the camera  184 , the millimeter-wave radar  186  and the laser radar  188  are electrically connected with the controller  182 . According to the image signal of the camera  184 , the millimeter-wave radar signal of the millimeter-wave radar  186  and the laser radar signal of the laser radar  188 , the controller  182  generates a vehicle-control signal to assist in driving the vehicle. The controller  182  determines whether to stop generating the vehicle-control signal as soon as receiving the alert signal from the processor  16 . 
     A deduction database is built using a plurality of reference rainfall values, a plurality of reference fog values, and a plurality of reference temperature/humidity values respectively acquired by the rainfall detector  10 , the fog detector  12 , and the temperature/humidity detector  14 , and the variations of the signals of the camera  184 , the millimeter-wave radar  186 , and the laser radar  188 . The processor  16  uses the fuzzy computation and the deduction database to generate the output value. 
     Besides, the processor  16  is further electrically connected with a storage device  20 , a steering angle sensor  22 , and a wheel velocity sensor  24 . The storage device  20  stores a slippage judgement equation. The processor  16  acquires the angular velocity of wheel from the steering angle sensor  22 , acquires the center velocity of wheel and rotation radius of wheel from the wheel velocity sensor  24 , and then substitutes them into the slippage judgement equation to generate a slippage value. If the processor  16  determines that the slippage value is greater than a preset slippage value, the processor  16  generates a slippage alert to the controller  182  of the automatic driver assistance device  18 . Once receiving the slippage alert, the controller  182  of the automatic driver assistance device  18  stops generating vehicle control signals. The processor  16  is also electrically connected with an illuminometer  26  generating an illumination value. If the process  16  determines that the illumination value is below a preset output value, the processor  16  generates an illumination alert to the controller  182  of the automatic driver assistance device  18  to inform the controller  182  that the illumination is too weak for the camera  184  to capture clear images. In such a case, the controller  182  would not generate vehicle control signals because the camera  184  cannot send image signals to the controller  182 . 
     The architecture of the system of the present invention has been described hereinbefore. The process of the method of the present invention will be described thereinafter. Refer to  FIG. 2  showing a flowchart of a method for detecting environment-induced disablement of ADAS, and refer to  FIG. 1  again. In Step S 10 , respectively acquire a rainfall value, a fog value and a temperature/humidity value via a rainfall detector  10 , a fog detector  12  and a temperature/humidity detector  14 . In Step S 12 , a processor  16  generates a rainfall-fog value according to the rainfall value and the fog value and generates a snowfall value according to the fog value and the temperature/humidity value. In Step S 14 , the processor  16  uses a fuzzy computation to process the rainfall-fog value and the snowfall value to generate an output value. In order to carry out the fuzzy computation, the rainfall detector  10 , the fog detector  12  and the temperature/humidity detector  14  respectively acquire a plurality of reference rainfall values, a plurality of reference fog values, and a plurality of reference temperature/humidity values. According to the rainfall values and the fog values, the processor  16  generates at least 9 rainfall-fog values, including rainless-fogless, light rain-fogless, heavy rain-fogless, rainless-thin fog, light rain-thin fog, heavy rain-thin fog, rainless-thick fog, light rain-thick fog, and heavy rain-thick fog. According to the fog value and temperature/humidity value, the processor  16  generates a snowfall value. The rainfall-fog values are integrated with the snowfall values to output the following 13 combinations: rainless-fogless-snowless, light rain-fogless-snowless, heavy rain-fogless-snowless, rainless-thin fog-snowless, light rain-thin for-snowless, heavy rain-thin fog-snowless, rainless-thick fog-snowless, light rain-thick fog-snowless, heavy rain-thick fog-snowless, rainless-fogless-light snow, rainless-thin fog-light snow, and rainless-thick fog-heavy snow. The output combinations are integrated with the information of the variations of the signals of the camera  184 , the millimeter-wave radar  186 , and the laser radar  188  to build a deduction database (a lookup list), which is presented in form of the curved surface shown in  FIG. 3 . Thus, the processor  16  can look up the corresponding point in the curved surface and find out a preset output value according to the intersection point of the rainfall-fog value and the snowfall value acquired through the rainfall detector  10 , the fog detector  12 , and the temperature/humidity detector  14 . 
     After the preset output value is obtained, the process proceeds to Step S 16 . In Step S 16 , the processor  16  determines whether the output value is over the preset output value. If the output value is not over the preset output value, the process returns to Step S 10 . If the output value is over the preset output value, the process proceeds to Step S 18 . In Step S 18 , the processor  16  generates an alert signal. Next, the process proceeds to Step S 20 . In Step S 20 , the automatic driver assistance device  18  determines whether to stop automatic driving according the alert signal. 
     The output value generated by the processor  16  is also used to evaluate the validness of the camera  184 , the millimeter-wave radar  186 , and the laser radar  188 . The evaluation process is described below. Firstly, the camera preset value, the millimeter-wave radar preset value and the laser radar preset value are established in the processor  16  beforehand for evaluating the validness of the camera  184 , the millimeter-wave radar  186 . The preset value is established according to the quality and performance of the device and the robustness of the algorithm thereof. Refer to  FIG. 4 . In evaluating the validness of the camera  184 , the processor  16  determines whether the output value generated in Step S 14  is over a camera preset value. In one embodiment, the camera preset value is set to be 0.25. The output values at the time points 2, 3, 5, 7, 8, 11, 12 and 13 all exceed 0.25. Thus, at these time points, the processor  16  generates a camera alert signal to the controller  182  of the automatic driver assistance device  18  to inform the controller  182  that the camera  184  is unable to generate image signals. Heavy rain, thick fog or heavy snow may disturb the lens of the camera  184  and hinder the camera  184  from capturing clear images. 
     In evaluating the validness of the laser radar  188 , the processor  16  determines whether the output value generated in Step S 14  is over the laser radar preset value. Refer to  FIG. 5 . In one embodiment, the laser radar preset value is set to be 0.5. The output values at the time points 2, 3, 5, 7, 8, 10 and 13 all exceed 0.5. Thus, at these time points, the processor  16  generates a laser radar alert signal to the controller  182  of the automatic driver assistance device  18  to inform the controller  182  that the laser radar  188  is unable to generate laser radar signals. At present, heavy rain, thick fog or heavy snow may impair function of the laser radar  188  and hinder the laser radar from generating correct laser radar signals. 
     In evaluating the validness of the millimeter-wave radar  186 , the processor  16  determines whether the output value generated in Step S 14  is over the millimeter-wave radar preset value. Refer to  FIG. 6 . In one embodiment, the millimeter-wave radar preset value is set to be 0.75. The output values at the time points 3 and 8 all exceed 0.75. Thus, at these time points, the processor  16  generates a millimeter-wave radar alert signal to the controller  182  of the automatic driver assistance device  18  to inform the controller  182  that the millimeter-wave radar  186  is unable to generate millimeter-wave radar signals. Heavy rain, thick fog or heavy snow may impair function of the laser radar  188  and hinder the millimeter-wave radar  186  from generating correct millimeter-wave radar signals. 
     In addition to using the output value to impalement the determination of whether the driver assistance device  18  continues generating vehicle control signals, the processor  16  further generates a slippage value in the following process: the processor  16  acquires the angular velocity of wheel from the steering angle sensor  22 , acquires the central velocity of wheel and rotation radius of wheel from the wheel velocity sensor  24 , and then substitutes them into the slippage judgement equation to generate a slippage value. The processor  16  determines whether the slippage value is greater a preset slippage value. If the processor  16  determines that the slippage value is greater than the preset slippage value, the processor  16  generates a slippage alert to the controller  182  of the automatic driver assistance device  18 . The slippage value over the preset slippage value indicates the vehicle slips severely. If the vehicle keeps running in such a case, it may cause a traffic accident. Therefore, on receiving the slippage alert, the controller  182  of the automatic driver assistance device  18  should stop automatic driving to avoid a traffic accident. The slippage judgement equation is expressed as
 
 S =( v−wr )/ v× 100%
 
wherein v is the center velocity (m/s) of wheel, w is the angular velocity (rad/s) of wheel, and r is the rotation radius (m) of wheel.
 
     The processor  16  also determines whether an illumination value detected by the illuminometer  26  is below a preset illumination value. If the illumination value is below the preset illumination value, the process  16  generates an illumination alert to the controller  182  of the automatic driver assistance device  18  to inform the controller  182  that the illumination is too low for the camera  184  to capture clear images and that the camera  184  is unable to generate image signals to the controller  182 . In such a case, the controller  182  does not generate vehicle control signals based on the image signals of the camera  184 . 
     In conclusion, the present invention can judge the influences of the external environment on ADAS and determine the reliability of the information provided by the cameras or radars of thereof to avoid receiving incorrect information from the cameras or radars operating in a low-reliability state. Therefore, the present invention can prevent ADAS from making erroneous determinations and exempt the vehicle from a traffic accident. Further, the present invention can judge the slippage of the vehicle and inform ADAS of the slippage to prevent the vehicle from keeping running in severe slippage and causing a traffic accident. 
     The embodiments described above are only to exemplify the present invention but not to limit the scope of the present invention. Any equivalent modification or variation according to the characteristic or spirit of the present invention is to be also included within the scope of the present invention.