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By Jordan Britt, David Bevly, and Christopher Rose Nearly half of all highway fatalities occur from unintended lane departures, which comprise approximately 20,000 deaths annually in the United States.  Studies have shown great promise in reducing unintended lane departures by alerting the driver when they are drifting out of the lane. At the core of these systems is a lane detection method typically based around the use of a vision sensor, such as a lidar (light detection and ranging) or a camera, which attempts to detect the lane markings and determine the position of the vehicle in the lane. Lidar-based lane detection attempts to detect the lane markings based on an increase in reflectivity of the lane markings when compared to the road surface reflectivity. Cameras, however, attempt to detect lane markings by detecting the edges of the lane markings in the image. This project seeks to compare two different lane detection techniques-one using a lidar and the other using a camera. Specifically, this project will analyze the two sensors’ ability to detect lane markings in varying weather scenarios, assess which sensor is best suited for lane detection, and determine scenarios where a camera or a lidar is better suited so that some optimal blending of the two sensors can improve the estimate of the position of the vehicle over a single sensor. Lidar-based lane detection The specific lidar-based lane detection algorithm for this project is based on fitting an ideal lane model to actual road data, where the ideal lane model is updated with each lidar scan to reflect the current road conditions. Ideally, a lane takes on a profile similar to the 100-averaged lidar reflectivity scans seen in Figure 1 with the corresponding segment. Figure 1. Lidar reflectivity scan with corresponding lane markings. Note that this profile has a relatively constant area bordered by peaks in the data, where the peaks represent the lane markings and the constant area represents the surface of the road.  An ideal lane model is generated with each lidar scan to mimic this averaged data, where averaging the reflectivity directly in front of the vehicle generates the constant portion and increasing the average road surface reflectivity by 75 percent mimics the lane markings.  This model is then stretched over a range of some minimum expected lane width to some maximum expected lane width, and the minimum RMSE between the ideal lane and the lidar data is assumed to be the area where the lane resides. For additional information on this method, see Britt, Rose & Levy, September 2011. Camera-based lane detection The camera-based method for this project was built in-house and uses line extraction techniques from the image to detect lane markings and calculate a lateral distance from a second-order polynomial model for the lane marking in image space. A threshold is chosen from the histogram of the image to compensate for differences in lighting, weather, or other non-ideal scenarios for extracting the lane markings. The thresholding operation converts the image into a binary image, which is followed by Canny edge detection. The Hough transform is then used to extract the lines from the image, fill in holes in the lane marking edges, and exclude erroneous edges. Using the slope of the lines, the lines are divided into left or right lane markings. Two criteria based on the assumption that the lane markings do not move significantly within the image from frame to frame are used to further exclude non-lane marking lines in the image. The first test checks that the slope of the line is within a threshold of the slope of the near region of the last frame’s second-order polynomial model. The second test uses boundary lines from the last frame’s second-order polynomial to exclude lines that are not near the current estimate of the polynomial. second-order polynomial interpolation is used on the selected lines’ midpoint and endpoints to determine the coefficients of the polynomial model, and a Kalman filter is used to filter the model to decrease the effect of erroneous polynomial coefficient estimates. Finally, the lateral distance is calculated using the polynomial model on the lowest measurable row of the image (for greater resolution) and a real-distance-to-pixel factor. For more information on this camera-based method, see Britt, et al. Figure 2. Camera-based lane detection (green-detected lanes,blue-extracted lane lines, red-rejected lines). Testing Testing was performed at the NCAT (National Center for Asphalt Technology) in Opelika, Alabama, as seen in Figure 3.  This test track is very representative of highway driving and consists of two lanes bordered by solid lane markings and divided by dashed lane markings.  The 1.7-mile track is divided into 200-foot segments of differing types of asphalt with some areas of missing lane markings and other areas where the lanes are additionally divided by patches of different types and colors of asphalt.   Figure 3. NCAT Test Facility in Opelika, Alabama. A precision survey of each lane marking of the test track as well as precise vehicle positions using RTK GPS were used in order to have a highly accurate measurement of the ability of the lidar and camera to determine the position of the vehicle in the lane. Testing occurred only on the straights, and the performance was analyzed on the ability of the lidar and camera to determine the position of the lane using metrics of mean absolute error (MAE), mean square error (MSE), standard deviation of error (σ­error), and detection rate. The specific scenarios analyzed included varying speeds, varying lighting conditions (noon and dusk/ dawn), rain, and oncoming traffic. Table 1 summarizes the results for these scenarios. For additional results, please see [8]. Scenario MAE(m) MSE(m) σ­error (m) %Det Lidar Noon Weaving 0.1818 0.1108 0.3076 98 Camera Noon Weaving 0.1077 0.0511 0.2246 80 Lidar Dusk 45mph 0.0967 0.0176 0.1245 100 Camera Dusk 45mph 0.2021 0.0592 0.2433 57 Lidar Medium Rain 0.1046 0.0177 0.1314 65 Camera Medium Rain 0.0885 0.0101 0.0635 91 Lidar Low Beam, Night 0.0966 0.0159 0.1215 99 Camera Low Beam, Night 0.1182 0.0185 0.0762 84 Table 1. Lidar and camera results for various environments. Additional testing on the effects of oncoming traffic at night was examined by parking a vehicle on the test track at a known location with the headlights on. Figure 4 shows the lateral error with respect to closing distance where a positive closing distance indicates driving at the parked vehicle, and a negative closing distance indicates driving away from the vehicle. Note that the camera does not report a solution at -200 m, which is due to track conditions and not the parked vehicle. Figure 4. Error vs. Closing Distance. Based on these findings it would appear that the camera provided slightly more accurate measurements than the lidar while having a decrease in detection rate. Additionally the camera performed well in the rain where the lidar experienced decreased detection rates. References Frank S. Barickman. Lane departure warning system research and test development. Transportation Research Center Inc., (07-0495), 2007. J. Kibbel, W. Justus, and K. Furstenberg. using multilayer laserscanner. In Proc. Lane estimation and departure warning Proc. IEEE Intelligent Transportation Systems, pages 607 611, September 13 15, 2005. P. Lindner, E. Richter, G. Wanielik, K. Takagi, and A. Isogai. Multi-channel lidar processing for lane detection and estimation. In Proc. 12th International IEEE Conference on Intelligent Transportation Systems ITSC ’09, pages 1 6, October 4 7, 2009. K. Dietmayer, N. Kämpchen, K. Fürstenberg, J. Kibbel, W. Justus, and R. Schulz. Advanced Microsystems for Automotive Applications 2005. Heidelberg, 2005. C. R. Jung and C. R. Kelber, “A lane departure warning system based on a linear-parabolic lane model,” in Proc. IEEE Intelligent Vehicles Symp, 2004, pp. 891–895. C. Jung and C. Kelber, “A lane departure warning system using lateral offset with uncalibrated camera,” in Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, sept. 2005, pp. 102 – 107. A. Takahashi and Y. Ninomiya, “Model-based lane recognition,” in Proc. IEEE Intelligent Vehicles Symp., 1996, pp. 201–206. Jordan Britt, C. Rose, & D. Bevly, “A Comparative Study of Lidar and Camera-based Lane Departure Warning Systems,” Proceedings of ION GNSS 2011, Portland, OR, September 2011.

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Apple m4551 studio display 24v dc 1.875a 45w used power supply.toshibapa2521u-3aca ac adapter 15vdc 6alaptop power supply,audiovox ild35-090300 ac adapter 9v 300ma used 2x5.5x10mm -(+)-,hipro hp-ow135f13 ac adapter 19vdc 7.1a -(+) 2.5x5.5mm used 100-.cge pa009ug01 ac adapter 9vdc 1a e313759 power supply.tatung tps-048 ac adapter 12vdc 4a -(+) 2.5x5.5mm 100-240vac ite.in contrast to less complex jamming systems.as many engineering students are searching for the best electrical projects from the 2nd year and 3rd year,fit mains fw7218m24 ac adapter 24vdc 0.5a 12va used straight rou.ibm 85g6708 ac dc adapter 16v 2.2a power supplycondition: used,hp compaq pa-1900-15c2 ac adapter 19vdc 4.74a desktop power supp,6 different bands (with 2 additinal bands in option)modular protection,pa-0920-dvaa ac adapter 9v dc 200ma used -(+) power supply,atlinks 5-2520 12v ac adapter 450ma 11w class 2 power supply,delta adp-40wb ac adapter 12vdc 3330ma -(+) 2x5.5mm used 100-240.novus dc-401 ac adapter 4.5vdc 100ma used 2.5 x 5.5 x 9.5mm.ibm 92p1044 ac adapter 16v dc 3.5a used 2.5 x 5.5 x 11.1mm.safe & warm 120-16vd7p c-d7 used power supply controller 16vdc 3,leinu70-1120520 ac adapter 12vdc 5.2a ite power supply desktop,d-link psac05a-050 ac adapter 5vdc 1a used -(+) 2x5.5x9mm round,edac ea12203 ac adapter 20vdc 6a used 2.6 x 5.4 x 11mm,mobile jammer india deals in portable mobile jammer,elpac power systems 2180 power supply used +8vdc 4a 32w shielded.alnor 350402003n0a ac adapter 4.5vdc 200ma used +(-) 2 x 4.8 x 1,pelouze dc90100 adpt2 ac adapter 9vdc 100ma 3.5mm mono power sup,skil 2607225299 ac adapter smartcharge system 7vdc 250ma used.hp c5160-80000 ac adapter 12v dc 1.6a adp-19ab scanjet 5s scanne,audiovox cnr505 ac adapter 7vdc 700ma used 1 x 2.4 x 9.5mm.this also alerts the user by ringing an alarm when the real-time conditions go beyond the threshold values,2wire mtysw1202200cd0s ac adapter -(+)- 12vdc 2.9a used 2x5.5x10,sam a460 ac adapter 5vdc 700ma used 1x2.5mm straight round barre,1km at rs 35000/set in new delhi.brushless dc motor speed control using microcontroller.li shin 0317a19135 ac adapter 19vdc 7.1a used -(+) 2x5.5mm 100-2.alvarion 0438b0248 ac adapter 55v 2a universal power supply,finecom 3774 u30gt ac adapter 12vdc 2a new -(+) 0.8x2.5mm 100-24.sony acp-80uc ac pack 8.5vdc 1a vtr 1.6a batt 3x contact used po,toshiba pa8727u 18vdc 1.7a 2.2a ac adapter laptop power supply.motorola spn4366c ac adapter 8vdc 1a 0.5x2.3mm -(+) cell phone p.ast 230137-002 ac adapter 5.2vdc 3a 7.5vdc 0.4a power supply cs7,ktec ksaa0500080w1eu ac adapter 5vdc 0.8a used -(+)- 1.5 x 3.5 x, bluzzin ,mascot 2415 ac adapter 1.8a used 3 pin din connector nicd/nimh c,globtek gt-21089-1509-t3 ac adapter 9vdc 1.7a 15w used -(+)- 2.5.


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Samsung ad-6019 ac adapter 19vdc 3.16a -(+) 3x5.5mm used roun ba,dtmf controlled home automation system.oem ad-1590n ac adapter 15vdc 900ma - ---c--- + used 1.1 x 3.5 x,bell phones u090050d ac dc adapter 9v 500ma class 2 power supply,swingline mhau412775d1000 ac adapter 7.5vdc 1a -(+) 1x3.5mm used.hp compaq ppp014s ac adapter 18.5vdc 4.9a used 2.5x5.5mm 90° rou.aopen a10p1-05mp ac adapter 22v 745ma i.t.e power supply for gps,ideation industrial be-090-15 switching adapter 29.5vdc 1.5a cha,cui stack dv-1280 ac adapter 12vdc 800ma used 1.9x5.4x12.1mm.shenzhen rd1200500-c55-8mg ac adapter 12vdc 1a used -(+) 2x5.5x9,disrupting a cell phone is the same as jamming any type of radio communication,fellowes 1482-12-1700d ac adapter 12vdc 1.7a used 90° -(+) 2.5x5,when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition,this project uses arduino for controlling the devices.pepsi diet caffein- free cola soft drink in bottles.that is it continuously supplies power to the load through different sources like mains or inverter or generator,viewsonic api-208-98010 ac adapter 12vdc 3.6a -(+)- 1.7x4.8mm po,which is used to test the insulation of electronic devices such as transformers.sharp ea-18a ac adapter 4.5vdc 200ma (-)+ used 2 x 5.5 x 11.7mm.palmone dv-0555r-1 ac adapter 5.2vdc 500ma ite power supply,fujitsu ca01007-0520 ac adapter 16v dc 2.7a new 4.5x6x9.7mm,casio ad-c 52 g ac dc adapter 5.3v 650ma power supply,compaq pp007 ac adapter 18.5vdc 2.7a used -(+)- 1.7x4.8mm auto c.this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,l.t.e lte12w-s2 ac adapter 12vdc 1a 12w power supply,ridgid r840091 ac adapter 9.6-18v 4.1a used lithium ion ni-cad r,sos or searching for service and all phones within the effective radius are silenced.power solve psg60-24-04 ac adapter 24va 2.5a i.t.e power supply,axis a31207c ac adapter 12vac 500ma used 2.5x5.5 x 11.3mm 90 deg.fisher price pa-0610-dva ac adapter 6vdc 100ma power supply.healthometer 4676 ac adapter 6vdc 260ma used 2.5x5.5mm -(+) 120v.sony pcga-ac19v9 ac adapter 19.5vdc 7.7a used -(+) 3.1x6.5x9.4mm.a blackberry phone was used as the target mobile station for the jammer,but are used in places where a phone call would be particularly disruptive like temples,most devices that use this type of technology can block signals within about a 30-foot radius.delta adp-10sb rev.h ac adapter 5vdc 2a 2x5.5mm hp compaq hewlet,globtek gt-41052-1507 ac adapter 7vdc 2.14a -(+) 2x5.5mm 100-240,wahl db06-3.2-100 ac adapter 3.2vdc 100ma class 2 transformer,symbol 50-14000-241r ac adapter 12vdc 9a new ite power supply 10,solytech ad1712c ac adapter 12vdc 1.25a 2x5.5mm used 100-240vac,this project shows the system for checking the phase of the supply,audiovox trc-700a cell phone battery charger used 6v 135ma btr-7.dell eadp-90ab ac adapter 20v dc 4.5a used 4pin din power supply,sunny sys1298-1812-w2 ac dc adapter 12v 1a 12w 1.1mm power suppl.

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