CN107408202A - Method and system for detecting ambient light - Google Patents

Abstract

The invention discloses a method and a system for detecting ambient light. The method includes capturing one or more viewable images by an image capture device, converting a color of each captured image to a gray color, determining a histogram for each gray captured image, calculating an average frequency average and a data average for the determined histograms, and at least one comparing the average frequency average to a predetermined FM threshold and the data average to a predetermined DM threshold for detecting ambient light.

Description

Method and system for detecting ambient light

field of invention

The field of the invention relates generally to ambient light detection, and more particularly to ambient light detection for vehicles.

Background of the invention

Existing vehicles use multiple sensors for various functions, such as for detecting ambient light, location, distance, speed, etc. In addition to adding to the complexity and structure of the solution, multiple sensors add to the overall cost of the system used to detect ambient light.

Ambient light sensing is required in a variety of applications where ambient light must be sensed. For example, for automotive applications, in high-end vehicles, ambient light sensors are used to adjust the backlight intensity of the instrument cluster or the LCD backlight of GPS devices or DVD screens. In addition, ambient light needs to be detected in various scenarios, such as when a vehicle enters a tunnel, under a bridge/under a tree canopy, etc. Additionally, ambient light sensing is required to automatically adjust the camera mode from day to night based on the sensed ambient light.

For ambient light detection, existing systems use optical/photoelectric sensors typically used for light detection, such as photodiodes or phototransistors. Existing tunnel detection methods usually use two environmental sensors. The first sensor has a wide field of view, while the other sensors have a narrow field of view. It senses the ambient light conditions in front of the vehicle. The sensor reacts quickly to sudden changes in light. Two sensors combine to detect the amount of light in the environment and automatically turn on/off the headlights.

Currently, ambient light sensors are present in high-end models. These sensors are basically optical sensors and are usually mounted on the windshield inside the vehicle. Sensors track various light levels in the environment and automatically turn on/off the headlights.

However, existing systems require a large number of sensors to detect ambient light. This increases cost and system complexity. Therefore, there is a need to reduce the number of sensors required to detect ambient light and provide a cost-effective and simple solution.

overview

Embodiments of the invention disclose a method and system for detecting ambient light. The method includes capturing one or more visual images, converting the captured images into gray images, determining a histogram for each gray image, computing the mean frequency mean (FM) and the data mean (DM) of the histogram, and Perform one of the following steps (a) compare FM with a predetermined FM threshold, and when the compared FM is greater than the predetermined FM threshold, detect ambient light as the best light, (b) compare FM with the predetermined FM threshold and comparing the DM with a predetermined DM threshold, and when the compared FM is less than the predetermined FM threshold and the compared DM is greater than the predetermined DM threshold, detecting ambient light as the best light, and (c) comparing the FM to the predetermined FM threshold and DM Compared with a predetermined FM threshold, and if the FM is less than the predetermined FM threshold and the DM is less than the predetermined DM threshold, the FM of the identified region of interest (ROI) is compared to the predetermined FM threshold, and when the compared ROI's When FM is less than a predetermined FM threshold, the detected ambient light is below optimal light.

According to embodiments herein, the method further comprises determining the FM of the identified region of interest (ROI): identifying the ROI in the captured image, and when the FM is less than a predetermined FM threshold, and when the DM value is less than a predetermined DM threshold , determine the FM of the identified region of interest (ROI).

According to the embodiments herein, calculating the FM of the determined histogram includes: determining the frequency average value of the determined histogram for a predetermined number of frames, and determining the FM for a predetermined number of frames.

According to embodiments herein, the method further comprises detecting time of day when the detected ambient light is higher than optimal light. Furthermore, when the detected ambient light is lower than optimal light, it is detected as possible entering and exiting the tunnel.

According to embodiments herein, the method further includes performing a predetermined control function based on detecting one or more states of ambient light.

According to another embodiment of the present invention, a system for detecting ambient light comprises: an image capture device adapted to capture one or more images, and a processing unit connected to the image capture device configured to perform The steps of: converting the color of each captured image to gray, determining a histogram of each gray captured image, calculating the mean frequency mean (FM) and data mean (DM) of the determined histogram, and perform one of the following steps: (a) compare the FM with a predetermined FM threshold, and when the compared FM is greater than the predetermined FM threshold, detect ambient light as the best light, (b) compare the FM with the predetermined FM threshold comparing, comparing the DM with a predetermined DM threshold, and when the compared FM is less than the predetermined FM threshold and the compared DM is greater than the predetermined DM value, detecting ambient light as the best light, and (c) comparing the FM with the predetermined The FM threshold is compared, the DM is compared with a predetermined DM threshold, and when the compared FM is smaller than the predetermined FM threshold and the DM is smaller than the predetermined DM threshold, the FM of the identified region of interest (ROI) is compared with the predetermined FM threshold A comparison is made, and when the FM of the compared ROI is less than a predetermined FM threshold, the ambient light is detected as less than optimal light.

Brief description of the drawings

The above-mentioned aspects and other features of the present invention are described below in conjunction with the accompanying drawings, wherein:

Fig. 1 shows a block diagram of a system for detecting ambient light according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of an input image and a corresponding histogram according to an embodiment of the present invention.

FIG. 3 is a flowchart of a method of detecting ambient light conditions according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of different ambient light conditions and corresponding histograms according to an embodiment of the present invention.

Figures 5a and 5b are schematic illustrations of time of day conditions with low-contrast images and good-contrast images, according to an embodiment of the invention.

Figures 6a and 6b are schematic diagrams of daytime time conditions when the average frequency mean (FM) is small but the data mean (DM) is high, according to an embodiment of the present invention.

Figures 7a and 7b are schematic diagrams of possible tunnel situations according to an embodiment of the invention.

Figure 8a is a schematic diagram of an entry tunnel according to an embodiment of the present invention.

Fig. 8b is a schematic diagram of tunnel conditions according to an embodiment of the present invention.

Fig. 9a is a schematic diagram of exit tunnel conditions according to an embodiment of the present invention.

Figure 9b is a schematic diagram of the final egress tunnel condition according to an embodiment of the present invention.

Detailed description of the invention

Embodiments of the present invention will now be described in detail with reference to the accompanying drawings. However, the present invention is not limited to these Examples. The present invention can be modified in various forms. Therefore, the embodiments of the present invention are merely provided to more clearly explain the present invention to those skilled in the art. In the drawings, the same reference numerals are used to denote the same components.

The specification will refer to "a", "an" or "some" in some places of the examples. This does not mean that each such reference refers to the same embodiment, or that a feature is only applicable to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments.

As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless expressly stated otherwise. It can be further understood that the terms "comprising", "comprising", "comprises" and/or "comprising" when used in this specification designate the presence of said features, integers, steps, operations, elements and/or components , but does not exclude the existence or addition of one or more other features, integers, steps, operations, elements, components and/or combinations thereof. As used herein, the term "and/or" includes any and all combinations and arrangements of one or more of the associated listed items.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will also be further understood that terms such as those defined in commonly used dictionaries should be construed to have a meaning consistent with their meaning in the context of the relevant field, and not be interpreted in an idealized or overly formal manner unless expressly so defined. In one embodiment, the mean frequency mean is interchangeably referred to as FM and FM _mean .

The present invention describes methods and systems for detecting changing ambient light conditions in an environment.

Fig. 1 shows a block diagram of a system for detecting ambient light according to an embodiment of the present invention. The system includes an image capture device 101 , a processing unit 102 , and a display unit 103 . The image capture device 101 is adapted to capture one or more images. The processing unit 102 is connected to the image capture device 101 to process captured images. The processing unit 102 is adapted to perform the steps comprising: converting the color of each captured image into gray, determining a histogram of each converted gray image, calculating a data mean (DM) and a mean frequency mean of the determined histograms (FM), comparing the mean frequency average to a predetermined FM threshold for detecting ambient light. Furthermore, the processing unit 102 compares the data average with a predetermined DM threshold when the average frequency average is less than a predetermined FM threshold for detecting ambient light. When the detection of ambient light is below optimal light, the system sends an alert to the user. In one embodiment, the visual alert is displayed on the display unit 103 .

In one embodiment, the method and system are used to detect ambient light using a vehicle-mounted front image capture device 101 , such as a camera 101 in a vehicle. The front camera in the vehicle can be an existing camera of a driver assistance system or it can be retrofitted.

In this embodiment, the method of the present invention detects ambient light under various road scene conditions, including but not limited to:

· Normal daylight

·Enter/Exit Tunnel

· In tunnels/entering parking lots/garages/closed areas, etc.

· Bridges / shadows due to tree crowns, etc.

When the vehicle is moving, the front camera 101 in the vehicle captures images of the surroundings in front of the vehicle. The captured images are then processed by the processing unit 102, wherein ambient light and road conditions are detected by the processing unit's control logic. Once it detects that the ambient light is below optimal, the processing unit sends an alert to the user. In one embodiment, the alert is in the form of a message displayed on the display unit 103 . In another embodiment, the electronic control unit 104 (ECU) performs predetermined control functions based on the detected ambient light. The processing unit sends signals to an electronic control unit 104 (ECU) of the lighting, which sequentially controls one or more vehicle functions, such as the headlights 105 . Predetermined control functions, including but not limited to adjusting headlights, dashboard lights, and turning on lights automatically according to road conditions.

Fig. 2 shows a schematic diagram of an input image and a corresponding histogram according to an embodiment of the present invention. The image captured by the front camera 101 of the vehicle is a color image as shown in FIG. ).

Fig. 3 shows a flowchart of a method of detecting ambient light conditions according to an embodiment of the present invention. In step 301, the front camera 101 captures a color image of the surroundings in front of the vehicle. In step 302, the captured color image is processed by the processing unit 102 and converted into a gray image. In step 303, a histogram of the processed image is determined. In step 304, according to the determined histogram of the image, a data average providing the average brightness (data mean/DM) of the image and an average of frequency values of the histogram are calculated. At step 305, the calculated FM is compared to a predetermined FM threshold. At step 306, if the FM is less than the predetermined FM threshold, the calculated DM is compared to the predetermined DM threshold. At step 307, based on the comparison, ambient light conditions are determined using defined indicators and thresholds. At step 308, appropriate alerts/warnings are given/displayed based on the changed values of DM and FM relative to predefined thresholds.

According to an embodiment of the present invention, the system for detecting ambient light is considered to be active during daytime hours/sufficient ambient light. An average frequency mean (FM) is calculated for the current scene, and the calculated FM is compared to a predetermined FM threshold. If the FM is greater than a predetermined FM threshold, the scene is identified as daytime and the ambient light is detected as optimal light. If FM is less than a predetermined FM threshold, a data mean (DM) is calculated for the current scene, and if DM is greater than a predetermined DM threshold, the scene is identified as daytime and the ambient light is detected as optimal light. Further, if FM is less than a predetermined FM threshold and DM is less than a predetermined DM threshold, a small region in the center of the image (region of interest) is selected, and then the FM of the selected ROI is calculated. Ambient light is detected as less than optimal light when the FM of the ROI being compared is less than a predetermined FM threshold.

According to another embodiment of the invention, it is considered that the system for detecting ambient light is being activated in nighttime/darker areas/lower ambient light areas. Computes the mean frequency mean (FM) and data mean (DM) for the current scene. FM and DM are compared to predetermined thresholds. If FM is greater than a predetermined FM threshold and DM is greater than a predetermined DM threshold, the scene is identified as daytime and the ambient light is detected as optimal light. If FM is less than the predetermined FM threshold and DM is greater than the predetermined DM threshold, the scene is identified as daytime and the ambient light is detected as optimal light. Further, if FM is less than a predetermined FM threshold and DM is less than a predetermined DM threshold, a small region in the center of the image (region of interest) is selected, and then the FM of the selected ROI is calculated. Ambient light is detected as less than optimal light when the FM of the ROI being compared is less than a predetermined FM threshold.

"Optimal light" as referred to herein includes, but is not limited to, light above a predetermined threshold, bright light conditions, and daylight conditions. Reference herein to "below optimal light" includes, but is not limited to, light below a predetermined threshold, low light conditions, nighttime conditions, tunnel, parking, garage, under bridge, tree canopy, etc. light. When sub-optimal light conditions are detected, the system displays appropriate warnings to the driver and the ECU controls the appropriate vehicle functions.

For example, in one embodiment, the method of the present invention detects possible tunnel conditions - entering a tunnel, entering a tunnel, and exiting a tunnel. A method for detecting possible tunnel conditions is described in detail in the further description. If the FM of the selected ROI of the image is less than a predetermined FM threshold, the system warns that the area ahead may be a tunnel and issues a warning message on the vehicle display ("Tunnel possible"). If this condition persists for a certain number of "n" frames, the warning message will change to "Enter Tunnel", eg 10 frames and then the "Enter Tunnel" flag will be set. Once the "Enter Tunnel" flag is set, the vehicle is assumed to be in the tunnel area and the warning message "In Tunnel" is displayed. The FM monitors the in-tunnel condition, and if it drops much below a predetermined FM threshold, the system warns it may be an out-of-tunnel condition. Therefore, an "exit tunnel" warning message will be provided and an "exit tunnel" flag will be set. A "Leaving Tunnel" warning is displayed, for example, about 15 frames. The final exit is monitored after 15 frames of leaving the condition until FM becomes greater than a predetermined FM threshold. Once FM becomes greater than a predetermined FM threshold, it indicates optimum light and daylight conditions, and all indicators reset accordingly.

According to an embodiment of the invention, the algorithm for identifying possible scenarios comprises the following steps:

a. Histogram calculation (total number of groups = 256)

i. Calculate the group value of the histogram

ii. Calculate the maximum value of 256 groups

iii. Scale the group value with the largest value

b. Frequency average and data average

i. Calculate the average of the group values of the scale (frequency average)

ii. Calculate the data value (data mean) from the group value

C. Road Scene Recognition - Setting Threshold

i. Calculate the frequency average over 5 frames (FM)

ii. Set thresholds for FM and DM (FM_THR, DM_THR, Exit_THR) d. Road scene recognition - method

i. During the day

1. If FM>FM_THR

2. Else FM < FM_THR but DM > DM_THR

3. Otherwise FM < FM_THR and DM < DM_THR but FM_ROI > FM_THR

a. Counter 1 = Counter 1-1;

ii. Possible Tunnel

1. If FM<FM_THR, DM<DM_THR and FM_ROI<FM_THR

2. Counter 1 = Counter 1+1;

iii. Enter the tunnel

1. If counter 1 > 5, mark = 0 in the tunnel

2. Counter 2 = Counter 2+1;

3. Display "Enter Tunnel" repeatedly until counter 2 = 10

4. Mark in the tunnel = 1

iv. In the tunnel

1. If counter 2 > 10 and mark in tunnel = 1 and FM > FM_THR

v. leave

1. If flag = 1 in tunnel, counter 2 > 10, but FM < exit_THR

2. Leave sign = 1

3. Counter 3 = Counter 3+1

v. leave last

1. If counter 3 == 15

2. If FM<exit_THR, display 'exit' mark

3. Otherwise, if FM>FM_THR, display the 'day' mark

The steps for detecting ambient light, such as time of day, possible tunnel, entering tunnel, within tunnel, exiting tunnel and final exit, are detailed below.

a. Histogram calculation

For a given grayscale image I, a histogram of size (m1xm2) with intensities (n) arranged from 0...L-1, is computed as follows:

Step 1: Calculate the histogram group values

Let 'p' denote the histogram group of the image and 'i' denote the group index

Then,

pi = total number of pixels with intensity ni

Among them, i=1...L-1(L=256) Formula 1

Step 2: Calculate the maximum frequency value

Computes the maximum of the computed histogram group values. This value is provided as the maximum frequency value.

Max_p = Max(pi) (i=0...L-1) Formula 2

Step 3: Scale group values with maximum frequency value

The scaled group value at the maximum frequency value gives each histogram a different frequency mean, which provides information about the distribution. Details are explained in the next section.

b. Frequency average and data average

A histogram provides a frequency distribution or a distribution of brightness values.

According to an embodiment of the present invention, FIG. 4 shows a schematic diagram of different ambient light conditions and corresponding histograms. Frequency distributions for various road conditions such as daytime, entering tunnel, inside tunnel, leaving tunnel, final exit (outside tunnel), etc. are disclosed. The histogram is generated for different ambient light conditions and is different for each road condition. In the case of a non-normalized histogram, for example, the sum of the frequencies of the histogram groups is equal to the size of the image, and in the case of a normalized histogram, it is equal to 1.

To obtain singular values that can provide information about the frequency distribution, the maximum frequency value of the frequency distribution is calculated and the histogram values are scaled.

P _i =P _i /maximum value p Equation 4

Group values are calculated as follows

in

L=256, scaling factor=height of the image

The histograms are close to zero or 255 for the entering tunnel or exiting tunnel conditions respectively, so the maximum frequency values will remain similar and the frequency averages will also be in the same range. To avoid erroneous decisions, the data/brightness averages of the images are also compared since the frequency averages are also in the same range.

Calculates the data average or brightness value of an image from a histogram. The histogram data obtained by Equation 1 were normalized as follows:

pi ranges between 0 and 1

C. Scene Recognition: Setting Thresholds

It is assumed that the system will be turned on during daytime conditions to recognize ambient light conditions. Example ranges of frequency averages and data averages for various ambient light conditions are described below:

Table 1: Analysis results of 2 videos to calculate frequency average and threshold for data average.

As shown in Table 1, the daytime brightness values range from 90 to 150, while FM is above 30. To clearly distinguish between daytime hours and low ambient light scenarios, the predetermined DM threshold is set to 80 (DM_THR). It can be observed that there are fluctuations in the frequency mean, especially when vehicles pass over bridges or tree canopies. To avoid false detections, the average of the frequency means is calculated. For the current frame, the average value of previous frames is stored and estimated. Update FM as follows:

Initially, the frequency averages for the first K frames are stored in an array, then, for the current frame, the averages for the previous K frames are calculated as follows:

Update: FM(i)=FM(i+1)....for i=1..k

FM(k)=FM (FM of current image)

The threshold of FM is set to 30 (FM_THR). As shown in Table 1, in the egress state, the frequency mean drops suddenly. To differentiate exit conditions from daytime time conditions, the exit threshold for FM is set to 20 (Exit_THR).

FM_THR 30 DM_THR 80 Exit_THR 20

Table 2: System-Defined Thresholds

d. Methods for distinguishing road scenes

The following ambient light conditions/scenes are recognized using the system: daytime, possible tunnel, entering tunnel, within tunnel, exiting tunnel and final exit.

Figures 5a and 5b show schematic diagrams of daytime time situations with low-contrast images and good-contrast images, according to an embodiment of the invention. As shown in Fig. 5a, for example, the FM value is 54.2 and the DM value is 101.2 and FM and DM are above their thresholds of 30 and 80, respectively. Also in Figure 5b, the FM value is 48.7 and the DM value is 128.05, where both values are above their thresholds of 30 and 80, respectively. Thus, if the FM is greater than a predetermined FM threshold (FM_THR), the current frame is marked as "time of day". Takes an average of previous frames, so you can avoid wrongly determining road conditions if the ambient light suddenly decreases.

Figures 6a and 6b show schematic diagrams of daytime time conditions in which the average frequency mean (FM) is small but the data mean (DM) is high, according to an embodiment of the present invention. As shown in Figure 6b, the FM value is 7.28 and the DM value is 87.5. At this time, the FM is less than the predetermined FM threshold (FM_THR) 30, but the DM of the current image is greater than the predetermined DM threshold (DM_THR) 80, so the current frame is marked as "daytime".

Further, as shown in Figure 6a, the FM value is 29.02 and the DM value is 68.64. Here, both FM and DM are smaller than their respective thresholds of 30 and 80, thus extracting a fixed width and height region of interest (ROI) in the center of the image. The FM of the current ROI is compared with a predetermined FM threshold (FM_THR) and if found larger, the current frame is marked as "daytime".

Figures 7a and 7b show schematic diagrams of possible tunnel situations, according to an embodiment of the invention. If the FM of the current ROI is smaller than a predetermined FM threshold (FM_THR) in the above last case as shown in FIG. 6, the current frame is marked as "possible tunnel". This condition is monitored by incrementing the count value (Counter_1) up to five frames.

Figures 8a and 8b show schematic views of conditions entering and inside a tunnel, according to an embodiment of the invention. If the count value (counter_1) is greater than 5, the current frame flag is switched to "entering the tunnel" as shown in FIG. 8a. If a daytime scenario is encountered, the count (counter_1) will be decremented. After five frames, the entering tunnel status will remain until the next ten consecutive frames (counter_2), after which the "in tunnel" flag will be set as shown in FIG. 8b. Until the FM drops below the Exit Threshold (Exit_THR), the current frame will be marked "in the tunnel".

According to an embodiment of the present invention, Fig. 9a shows a schematic diagram of exiting a tunnel situation. Exiting the tunnel is only monitored unless marked in the tunnel is set. If the FM falls below the Exit Threshold (Exit_THR), the current frame is marked as "Exit Tunnel" and the count of Exit (Counter_3) is incremented. At the same time, during the exit period, it can be observed that the brightness value increases sharply. This condition can also be monitored to avoid false detection of egress conditions. The exit status is displayed for about 15 frames (counter_3). Thereafter, set the exit tunnel sign and monitor the final exit status.

Figure 9b shows a schematic diagram of the final egress tunnel condition, according to an embodiment of the present invention. If the FM is less than the exit threshold (Exit_THR), the current frame is marked as "Leaving the Tunnel", otherwise, the current frame is marked as "Daytime". Both flags and counters are reset to detect more tunnel-like conditions.

Setting three counters makes switching from one scene to another not abrupt. The first counter (counter_1) monitors 5 frames for possible tunnel conditions. If a "time of day" condition is encountered, counter_1 is decremented. The second counter (Counter_2) monitors the In-Tunnel status and maintains the "Enter-Tunnel" condition until the next consecutive 10 frames. The third counter (Counter_3) maintains the exit tunnel condition for approximately 15 frames.

The two additional flags (In Tunnel and Exit flag) ensure that switching from the In Tunnel condition to the Daytime condition and from the Exit condition to the Daytime condition does not occur in a random fashion. Once an entry into the tunnel is detected, the in-tunnel flag is set, which ensures that the next condition before reaching the exit condition will be the in-tunnel condition. Daytime conditions are monitored only after an exit flag is set, otherwise it is flagged to exit the tunnel.

The method and system of the present invention is a simple and accurate ambient light detection system. The detection method of the present invention helps to distinguish various low-light conditions, such as but not limited to tunnel-like conditions, parking, under bridges, tree crowns, and the like. The present invention provides appropriate warnings, such as "Possible Tunnel" and "Enter Tunnel", depending on the situation. In addition, the ECU performs predetermined control functions based on the detected amount of ambient light. The ECU controls one or more vehicle functions based on the detected ambient light. Pre-defined control functions include, but are not limited to, automatic adjustment of headlights, dashboard lights, and turn signals according to road conditions. According to the present invention, the overall classification of road scene conditions and the detection of ambient light are realized by using only two parameters of mean frequency mean (FM) and data mean (DM).

This invention is intended to cover all equivalents to those shown and described in the drawings of this application. The examples of embodiments used to illustrate the invention in no way limit the applicability of the invention to them. It should be noted that those skilled in the art will appreciate that various modifications and substitutions of details may be made on the overall teaching of the present disclosure without departing from the scope of the present invention.

Claims (8)

1. A method for detecting ambient light, comprising: capture one or more viewable images; convert the captured image into a gray image; determine the histogram of each gray image; calculating the average frequency mean (FM) and data mean (DM) of the determined histogram; and Do one of the following steps a) comparing FM to a predetermined FM threshold; and When the compared FM is greater than the predetermined FM threshold, the ambient light is detected as the best light; b) comparing FM to a predetermined FM threshold and comparing DM to a predetermined DM threshold; and Detecting ambient light as optimal light when the compared FM is less than a predetermined FM threshold and the compared DM is greater than a predetermined DM threshold; and c) comparing FM to a predetermined FM threshold and comparing DM to a predetermined DM threshold; and if FM is less than a predetermined FM threshold and DM is less than a predetermined DM threshold; comparing the FM of the identified region of interest (ROI) to a predetermined FM threshold; and Ambient light is detected as less than optimal light when the FM of the compared ROI is less than a predetermined FM threshold. 2. The method of claim 1, further comprising determining the FM of the identified region of interest (ROI): identifying a region of interest in the captured image; and The FM of the identified region of interest (ROI) is determined when the FM is less than a predetermined FM threshold and when the DM is less than a predetermined DM threshold. 3. The method of claim 1, wherein calculating the FM of the determined histogram comprises: For a predetermined number of frames, determining a frequency average of the determined histogram; and Determine the FM of the predetermined number of frames. 4. The method of claim 1, further comprising detecting time of day when the detected ambient light is higher than optimal light. 5. The method of claim 1, further comprising detecting possible entry into and exit from the tunnel when the detected ambient light is below optimal light. 6. The method of claim 1, further comprising executing a predefined control function based on the detected one or more conditions of ambient light. 7. A system for detecting ambient light comprising: an image capture device adapted to capture one or more images; and A processing unit connected to the image capture device, configured to perform the steps comprising: Convert the color of each captured image to gray; Determine the histogram of each gray captured image; calculating the average frequency mean (FM) and data mean (DM) of the determined histogram; and Do one of the following steps a) comparing FM to a predetermined FM threshold; and When the compared FM is greater than the predetermined FM threshold, the ambient light is detected as the best light; b) comparing the FM with a predetermined FM threshold; comparing the DM to a predetermined DM threshold; and Detecting ambient light as optimal light when the compared FM is less than a predetermined FM threshold and the compared DM is greater than a predetermined DM threshold; and c) comparing the FM with a predetermined FM threshold; Comparing the DM to a predetermined DM threshold; when the compared FM is less than a predetermined FM threshold and the DM is less than a predetermined DM threshold; comparing the FM of the identified region of interest (ROI) to a predetermined FM threshold; and When the FM of the compared ROI is less than a predetermined FM threshold, the detected ambient light is below optimal light. 8. The system of claim 7, wherein the processor is further configured to perform the step of determining the FM of the identified region of interest (ROI): identifying a region of interest in the captured image; and The FM of the identified region of interest (ROI) is determined when the FM is less than a predetermined FM threshold and the DM is less than a predetermined DM threshold.