| 1. |
- Johansson, Ted, 1959-, et al.
(författare)
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Improving angle-of-view for a 1-D sensing application by using a 2-D optical sensor in "cylindrical" mode
- 2021
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Ingår i: IEEE Sensors Letters. - : IEEE. - 2475-1472. ; 5:10
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Tidskriftsartikel (refereegranskat)abstract
- To further develop a low-power low-cost optical motion detector for use with traffic detection under dark and daylight conditions, we have developed and verified a procedure to use a Near Sensor Image Processing (NSIP) programmable 2-D optical sensor in a "1-D mode" to achieve the effect of using a cylindrical lens, thus improving the angle-of-view (AOV), the sensitivity, and usefulness of the sensor. Using an existing 256 x 256 element sensor in an innovative way, the AOV was increased from 0.4 to 21.3 in the vertical direction while also improving the sensitivity. The details of the sensor hardware architecture are described in detail and pseudo code for programming the sensor is discussed. The results were used to demonstrate the extraction of Local Extreme Points (LEPs) used for Time-To-Impact (TTI) calculations to estimate the speed of an approaching vehicle.
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| 2. |
- Johansson, Ted, et al.
(författare)
-
Improving Angle-of-View for a 1-D Sensing Application by Using a 2-D Optical Sensor in "Cylindrical" Mode
- 2021
-
Ingår i: IEEE SENSORS LETTERS. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1472. ; 5:10
-
Tidskriftsartikel (refereegranskat)abstract
- To further develop a low-power, low-cost optical motion detector for use with traffic detection under dark and daylight conditions, we have developed and verified a procedure to use a near-sensor image processing programmable 2-D optical sensor in a "1-D mode" to achieve the effect of using a cylindrical lens, thus improving the angle-of-view (AOV), the sensitivity, and usefulness of the sensor. Using an existing 256 x 256 element sensor in an innovative way, the AOV was increased from 0.4. to 21.3. in the vertical direction while also improving the sensitivity. The details of the sensor hardware architecture are described in detail and pseudo-code for programming the sensor is discussed. The results were used to demonstrate the extraction of local extreme points used for time-to-impact calculations to estimate the speed of an approaching vehicle.
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| 3. |
- Karlsson, Rickard, 1970-, et al.
(författare)
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Speed Estimation From Vibrations Using a Deep Learning CNN Approach
- 2021
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Ingår i: IEEE Sensors Letter. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1472 .- 2475-1472. ; 5:3
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- A novel method for accurate speed estimation of a vehicle using a deep learning convolutional neural network (CNN), with accelerometer and gyroscope measurements as input, is presented. It does not suffer from the fundamental drift problem present in all dead reckoning methods, and yet yields about 2 m/s in accuracy. Efficient drift-free vehicle speed estimates are essential in many automotive applications, where internal wheel speed sensors or GPS are unavailable. Using extensive experimental data, the proposed CNN method is compared to an existing frequency analysis method. The proposed method is shown to perform significantly better, particularly during low speed and rapid speed changes where the frequency method struggles.
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