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Labview based automated car lighting

ramya c b
ramya c b

This document describes a LabVIEW-based system to automatically control a vehicle's headlight beam (high vs low) based on detected light intensity levels. A photo detector measures the intensity of oncoming light and a stepper motor tilts the headlight reflector up or down accordingly. The system aims to reduce glare for drivers by adjusting the headlights based on the situation rather than requiring manual input. It also uses image analysis to detect fog and automatically switch on/off front and rear fog lamps. The goal is to improve safety and driver comfort through automated headlight control.

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LABVIEW BASED AUTOMATED CAR LIGHTING Akshatha N1 , Ramya C B2 , Narendra Kumar3 1 PG student in Electronics and Communication, RNSIT 2 PG student in Electronics and Communication, RNSIT 3 Assistant Professor, Department of Electronics and Communication, RNSIT 1 akshathan265@gmail.com 2 ramyacb541@gmail.com 3 nkrnsit@gmail.com Abstract It is always annoying while travelling in nights due to headlight glare. It is primarily due to high intensity light rays belonging to the headlight of the oncoming vehicle. Street lamps and presence of fog at night can also cause a similar problem. In the presence of fog at night, the driver gets dazzled by his own high beam which can result in catastrophe. If the vehicle could detect fog at night, the high beam assistant could be adjusted accordingly. Modern vehicles come with manual headlamp control, but some situations may result in accidents due to carelessness or ignorance of the driver. If two vehicles are approaching each other with their headlight in high beam, it produces a very high glare to both the drivers. Solution for this is to either switch the headlamp to low beam or reduce the intensity of the LED Headlamp. We can have a system which is continuously monitoring the intensity of light in the surrounding and based on which it takes a decision of switching to high beam or low beam. The intensity of the light which is opposite us is detected by using a photo detector and it is processed. Wavelength of the visible light varies from 400 to 750 nm, so we can use the Si or Ge photo detector. By means of the photo detector, the optical signal is converted into an electrical signal and then this electrical signal is suitably converted to a digital signal. This is given to the monitoring system for decision making. A stepper motor is used to tilt the angle of the reflector on which the LED Headlamp is fixed, based on the program executed by the Monitoring System. Keywords Photo Detector (Si or Ge), LED Headlamp, 1.8属 step Stepper motor, Fog Detection I. INTRODUCTION In automobiles during night travels, it will be annoying when there is a pronounced headlight glare. In most of the automobiles there is a manual device for switching between high beam and low beam. But the drivers prefer not to use them because of the labour involved in addition to the work of driving. Hence it may lead to poor visibility, discomfort for drivers and insecurity for passengers. In that aspect, we tried through Virtual Instrumentation, by Using Lab VIEW, to solve the problem. We implemented automated tilting of the reflector in the headlamp to ease the driver off a possible high intensity glare. The possible solution to the problem is to automate the process of tilting of headlamp reflector based on the measured light intensity. Based on a survey conducted in USA, the intensity value is standardized. We have fixed an intensity limit for glare value. A light sensor is fixed on the front side of the vehicle. The measured light intensity at every point of time is compared with the intensity limit value and a decision is taken whether to tilt the reflector up or down. Thus the glare and hence a possible accident is averted. LIGHT INTENSITY VALUE(in lux) VISUAL RESPONSE 0 to 0.25 Unnoticeable 0.25 to 0.75 Satisfactory 0.75 to 2 Just admissible 2 to 4 Disturbance Above 4 Unbearable Table.1. Shows the Visual response to Light Intensity Fig.1. Top: Low Beams point downward onto the road, while high beams point upward. Bottom: Night time scene imaged with high beams off (left) and on (right) II. IMAGE BASED FOG DETECTION The presence of Fog can be detected by using Image Sensing techniques [4]. Modern vehicles are equipped with many cameras and their use in many practical applications is extensive. We can detect the presence of fog from images of a camera mounted in vehicles.
We can analyse different properties of local objects in the image like lane markings, traf鍖c signs, back lights of vehicles in front or head lights of approaching vehicles to detect presence of fog. But contrast to all these related works we can use image descriptors [1] and a classi鍖cation procedure to distinguish images with fog present from those free of fog. These image descriptors are global and describe the entire image using Gabor 鍖lters at different frequencies, scales and orientations. In computer vision, visual descriptors or image descriptors are descriptions of the visual features of the contents in images, videos, algorithms, or applications that produce such descriptions. They describe elementary characteristics such as the shape, the colour, the texture or the motion, among others. We have different categories of fog, indicating their severity. The following are the different fog categories [3]: Visibility distance above 1000 m: No Fog Visibility distance between 300 and 1000 m: Low Fog Visibility distance between 100 and 300 m: Fog Visibility distance below 100 m: Dense Fog Fig.2. Examples images for labelling categories. From left to right: No Fog, Low Fog, Fog and Dense Fog If the presence and occurrence of fog could be recognized by vehicle, its front and rear fog lamp could be automatically switched on or off, as it is often done for low beam and high beam light in todays vehicles A system was introduced [2] to estimate the visibility range through the use of a camera mounted inside a vehicle. It aims to cover all possible situations of low visibility caused by dazzling, rain, snow or fog. The visibility range is thereby estimated by the attenuation of the contrast along similar road features like lane markings, banquet or even oil stripes. Fig.3. Images of Fog and Fog free scenes III. PHOTODETECTORS Photo detectors are used primarily as an optical receiver to convert light into electricity. The principle that applies to photo detectors is the photoelectric effect, which is the effect on a circuit due to light. A photo detector operates by converting light signals that hit the junction to a voltage or current. The junction uses an illumination window with an anti- reflect coating to absorb the light photons. The result of the absorption of photons is the creation of electron-hole pairs in the depletion region. The conductivity of photodiodes is as follows: The value th is the thermal conductance and is also referred to as dark current when no light hits the junction. The photodiode conductivity ph can be represented by the electron hole charge carrier concentrations: The value 袖n is the mobility of electrons and n is the charge concentration of electrons while 袖p is the mobility of holes and p is the charge concentration of holes IV. SYSTEM SETUP The setup consists of the following components: A photo detector An Image Descriptor for Fog Detection NI MYDAQ Card UNI-STEP 1.8属 step, AC stepper motor Reflector beam operated on 12V battery VI program working on Lab VIEW 8.2 platform A tilting mechanism The steps involved: The headlamp is positioned by the actuating mechanism. The external light Intensity is measured by a Photo detector The measured value is fed to the LAB VIEW Program through MYDAQ Based on measured value, LAB VIEW executes the program Based on the output of the program, MYDAQ Actuates the stepper motor steps Now if there is any fog in the surrounding of the vehicle, its presence is detected by the Image Descriptors. Based on the presence of Fog, the vehicles Front and Rear Fog Lamps are automatically switched on or off without the drivers intervention. There are several constraints in the actuating process. They are explained below. The maximum angle of tilt should not exceed 45属. The initial position of the head lamp is to be specified by the user. It is assumed that 0属 corresponds to high beam and 45属 corresponds to low beam. The assumed motor speed is 200rpm.
The flow diagram is as follows: Fig.4. The Flow Diagram V. THE ALGORITHM The algorithm of the block diagram is explained below: The sensed light intensity value in lux is compared with the intensity limit value (which is 0.75 lux, obtained based on survey results). The position of the reflector at that point of time is calculated based on its initial position, specified by the user. The calculation is nothing but the step angle (in our case, 1.8属) is added to the initial angular position of the head lamp in case of clockwise rotation, or subtracted from the initial angular position of the head lamp in case of anti-clockwise rotation. The decision to rotate the stepper motor shaft in clockwise or anticlockwise direction depends on two conditions. The angular position should be greater than 0属 and the light intensity should be less than 0.75 lux in order to rotate the shaft in the anti-clockwise direction. The angular position should be lesser than 45属 and the light intensity should be greater than 0.75 lux in order to rotate the shaft in the anti-clockwise direction. When the angular position reaches 0属 or 45属, the shaft should remain idle without rotating. VI. CONCLUSION It is evident that most of the drivers in the present world find it annoying to switch between high beam and low beam manually. Hence a system which does this automatically for the driver will be of immense help. Implementation of this setup can ease the vehicle driver off the labour to manually switch between high beam and low beam, thus saving a lot of time. It eases in maintaining the visibility and concentration of the driver thus helping in averting accidents. It is slightly costly compared to manual switches, but it is worth spending considering the advantages it offers. But the cost can be brought down by building a specific Monitoring System for both acquisition and actuation. This idea can be further extended to control the intensity of the LED headlamp by using PWM [Pulse Width Modulation]. If achieved, this can save a lot of power and also can control the glare to a very large extent. We can also interface the Photo detector sensor with the automobiles internal lighting system. This facilitates automatic turning on of the cabin lights during night time when the automobile is stationary. VII. REFERENCES [1] R. Gallen, A. Cord, N. Hauti竪re and D. Aubert, Towards Night Fog Detection through use of In-Vehicle multipurpose Cameras, Intelligent Vehicles Symposium, 2011 [2] D.A. Pomerleau, Visibility Estimation from a Moving Vehicle Using the RALPH Vision System, IEEE Conference on Intelligent Transportation System, 1997 [3] S. Bronte, L.M. Bergasa, L., and P.F. Alcantarilla, Fog Detection System Based on Computer Vision Techniques, IEEE Intelligent Transportation Systems, vol. 12, 2009 [4] N. Hauti竪re, J.P. Tarel, J. Lavenant and D. Aubert, Automatic fog detection and estimation of visibility distance through use of an on-board camera, Machine Vision and Applications, vol. 17, 2006 [5] Image based fog detection in vehicles Mario Pavlic, Heidrun Belzner, Gerhard Rigoll and Slobodan Ili 卒 c卒 [6] K即onning, T., Amsel, C., Ho鍖mann, Light has to go where it is needed: Future light based driver assistance systems. In: Proc. of the 7th International Symposium on Automotive Lighting, Darmstadt, Germany (2007) [7] Michigan Univ. Transportation Research Inst. Use of high- beam headlamps (2006) [8] Night time Vehicle Detection for Intelligent Headlight Control, Computer Vision Centre and Computer Science Dept., Aton. Univ. of Barcelona

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Labview based automated car lighting

  • 1. LABVIEW BASED AUTOMATED CAR LIGHTING Akshatha N1 , Ramya C B2 , Narendra Kumar3 1 PG student in Electronics and Communication, RNSIT 2 PG student in Electronics and Communication, RNSIT 3 Assistant Professor, Department of Electronics and Communication, RNSIT 1 akshathan265@gmail.com 2 ramyacb541@gmail.com 3 nkrnsit@gmail.com Abstract It is always annoying while travelling in nights due to headlight glare. It is primarily due to high intensity light rays belonging to the headlight of the oncoming vehicle. Street lamps and presence of fog at night can also cause a similar problem. In the presence of fog at night, the driver gets dazzled by his own high beam which can result in catastrophe. If the vehicle could detect fog at night, the high beam assistant could be adjusted accordingly. Modern vehicles come with manual headlamp control, but some situations may result in accidents due to carelessness or ignorance of the driver. If two vehicles are approaching each other with their headlight in high beam, it produces a very high glare to both the drivers. Solution for this is to either switch the headlamp to low beam or reduce the intensity of the LED Headlamp. We can have a system which is continuously monitoring the intensity of light in the surrounding and based on which it takes a decision of switching to high beam or low beam. The intensity of the light which is opposite us is detected by using a photo detector and it is processed. Wavelength of the visible light varies from 400 to 750 nm, so we can use the Si or Ge photo detector. By means of the photo detector, the optical signal is converted into an electrical signal and then this electrical signal is suitably converted to a digital signal. This is given to the monitoring system for decision making. A stepper motor is used to tilt the angle of the reflector on which the LED Headlamp is fixed, based on the program executed by the Monitoring System. Keywords Photo Detector (Si or Ge), LED Headlamp, 1.8属 step Stepper motor, Fog Detection I. INTRODUCTION In automobiles during night travels, it will be annoying when there is a pronounced headlight glare. In most of the automobiles there is a manual device for switching between high beam and low beam. But the drivers prefer not to use them because of the labour involved in addition to the work of driving. Hence it may lead to poor visibility, discomfort for drivers and insecurity for passengers. In that aspect, we tried through Virtual Instrumentation, by Using Lab VIEW, to solve the problem. We implemented automated tilting of the reflector in the headlamp to ease the driver off a possible high intensity glare. The possible solution to the problem is to automate the process of tilting of headlamp reflector based on the measured light intensity. Based on a survey conducted in USA, the intensity value is standardized. We have fixed an intensity limit for glare value. A light sensor is fixed on the front side of the vehicle. The measured light intensity at every point of time is compared with the intensity limit value and a decision is taken whether to tilt the reflector up or down. Thus the glare and hence a possible accident is averted. LIGHT INTENSITY VALUE(in lux) VISUAL RESPONSE 0 to 0.25 Unnoticeable 0.25 to 0.75 Satisfactory 0.75 to 2 Just admissible 2 to 4 Disturbance Above 4 Unbearable Table.1. Shows the Visual response to Light Intensity Fig.1. Top: Low Beams point downward onto the road, while high beams point upward. Bottom: Night time scene imaged with high beams off (left) and on (right) II. IMAGE BASED FOG DETECTION The presence of Fog can be detected by using Image Sensing techniques [4]. Modern vehicles are equipped with many cameras and their use in many practical applications is extensive. We can detect the presence of fog from images of a camera mounted in vehicles.
  • 2. We can analyse different properties of local objects in the image like lane markings, traf鍖c signs, back lights of vehicles in front or head lights of approaching vehicles to detect presence of fog. But contrast to all these related works we can use image descriptors [1] and a classi鍖cation procedure to distinguish images with fog present from those free of fog. These image descriptors are global and describe the entire image using Gabor 鍖lters at different frequencies, scales and orientations. In computer vision, visual descriptors or image descriptors are descriptions of the visual features of the contents in images, videos, algorithms, or applications that produce such descriptions. They describe elementary characteristics such as the shape, the colour, the texture or the motion, among others. We have different categories of fog, indicating their severity. The following are the different fog categories [3]: Visibility distance above 1000 m: No Fog Visibility distance between 300 and 1000 m: Low Fog Visibility distance between 100 and 300 m: Fog Visibility distance below 100 m: Dense Fog Fig.2. Examples images for labelling categories. From left to right: No Fog, Low Fog, Fog and Dense Fog If the presence and occurrence of fog could be recognized by vehicle, its front and rear fog lamp could be automatically switched on or off, as it is often done for low beam and high beam light in todays vehicles A system was introduced [2] to estimate the visibility range through the use of a camera mounted inside a vehicle. It aims to cover all possible situations of low visibility caused by dazzling, rain, snow or fog. The visibility range is thereby estimated by the attenuation of the contrast along similar road features like lane markings, banquet or even oil stripes. Fig.3. Images of Fog and Fog free scenes III. PHOTODETECTORS Photo detectors are used primarily as an optical receiver to convert light into electricity. The principle that applies to photo detectors is the photoelectric effect, which is the effect on a circuit due to light. A photo detector operates by converting light signals that hit the junction to a voltage or current. The junction uses an illumination window with an anti- reflect coating to absorb the light photons. The result of the absorption of photons is the creation of electron-hole pairs in the depletion region. The conductivity of photodiodes is as follows: The value th is the thermal conductance and is also referred to as dark current when no light hits the junction. The photodiode conductivity ph can be represented by the electron hole charge carrier concentrations: The value 袖n is the mobility of electrons and n is the charge concentration of electrons while 袖p is the mobility of holes and p is the charge concentration of holes IV. SYSTEM SETUP The setup consists of the following components: A photo detector An Image Descriptor for Fog Detection NI MYDAQ Card UNI-STEP 1.8属 step, AC stepper motor Reflector beam operated on 12V battery VI program working on Lab VIEW 8.2 platform A tilting mechanism The steps involved: The headlamp is positioned by the actuating mechanism. The external light Intensity is measured by a Photo detector The measured value is fed to the LAB VIEW Program through MYDAQ Based on measured value, LAB VIEW executes the program Based on the output of the program, MYDAQ Actuates the stepper motor steps Now if there is any fog in the surrounding of the vehicle, its presence is detected by the Image Descriptors. Based on the presence of Fog, the vehicles Front and Rear Fog Lamps are automatically switched on or off without the drivers intervention. There are several constraints in the actuating process. They are explained below. The maximum angle of tilt should not exceed 45属. The initial position of the head lamp is to be specified by the user. It is assumed that 0属 corresponds to high beam and 45属 corresponds to low beam. The assumed motor speed is 200rpm.
  • 3. The flow diagram is as follows: Fig.4. The Flow Diagram V. THE ALGORITHM The algorithm of the block diagram is explained below: The sensed light intensity value in lux is compared with the intensity limit value (which is 0.75 lux, obtained based on survey results). The position of the reflector at that point of time is calculated based on its initial position, specified by the user. The calculation is nothing but the step angle (in our case, 1.8属) is added to the initial angular position of the head lamp in case of clockwise rotation, or subtracted from the initial angular position of the head lamp in case of anti-clockwise rotation. The decision to rotate the stepper motor shaft in clockwise or anticlockwise direction depends on two conditions. The angular position should be greater than 0属 and the light intensity should be less than 0.75 lux in order to rotate the shaft in the anti-clockwise direction. The angular position should be lesser than 45属 and the light intensity should be greater than 0.75 lux in order to rotate the shaft in the anti-clockwise direction. When the angular position reaches 0属 or 45属, the shaft should remain idle without rotating. VI. CONCLUSION It is evident that most of the drivers in the present world find it annoying to switch between high beam and low beam manually. Hence a system which does this automatically for the driver will be of immense help. Implementation of this setup can ease the vehicle driver off the labour to manually switch between high beam and low beam, thus saving a lot of time. It eases in maintaining the visibility and concentration of the driver thus helping in averting accidents. It is slightly costly compared to manual switches, but it is worth spending considering the advantages it offers. But the cost can be brought down by building a specific Monitoring System for both acquisition and actuation. This idea can be further extended to control the intensity of the LED headlamp by using PWM [Pulse Width Modulation]. If achieved, this can save a lot of power and also can control the glare to a very large extent. We can also interface the Photo detector sensor with the automobiles internal lighting system. This facilitates automatic turning on of the cabin lights during night time when the automobile is stationary. VII. REFERENCES [1] R. Gallen, A. Cord, N. Hauti竪re and D. Aubert, Towards Night Fog Detection through use of In-Vehicle multipurpose Cameras, Intelligent Vehicles Symposium, 2011 [2] D.A. Pomerleau, Visibility Estimation from a Moving Vehicle Using the RALPH Vision System, IEEE Conference on Intelligent Transportation System, 1997 [3] S. Bronte, L.M. Bergasa, L., and P.F. Alcantarilla, Fog Detection System Based on Computer Vision Techniques, IEEE Intelligent Transportation Systems, vol. 12, 2009 [4] N. Hauti竪re, J.P. Tarel, J. Lavenant and D. Aubert, Automatic fog detection and estimation of visibility distance through use of an on-board camera, Machine Vision and Applications, vol. 17, 2006 [5] Image based fog detection in vehicles Mario Pavlic, Heidrun Belzner, Gerhard Rigoll and Slobodan Ili 卒 c卒 [6] K即onning, T., Amsel, C., Ho鍖mann, Light has to go where it is needed: Future light based driver assistance systems. In: Proc. of the 7th International Symposium on Automotive Lighting, Darmstadt, Germany (2007) [7] Michigan Univ. Transportation Research Inst. Use of high- beam headlamps (2006) [8] Night time Vehicle Detection for Intelligent Headlight Control, Computer Vision Centre and Computer Science Dept., Aton. Univ. of Barcelona