Research on Sensor Optimization Technology of Driverless Vehicle

Authors

  • Zhen Song
  • Hongwei Deng

DOI:

https://doi.org/10.54097/fcis.v4i2.10370

Keywords:

Environmental Perception, Driverless Cars, Sensor

Abstract

Driverless cars in operation, the perception of the surrounding environment demand is very rich, it is a kind of automatic detection of road information, detection of obstacles, calculation of obstacle location, speed and other functions, but due to the limitations of technology and detection means, the perception data of self-driving cars is not accurate enough, prone to safety accidents. Therefore, the optimization of various sensors can greatly improve the safety performance of unmanned vehicles, thereby greatly promoting the development of unmanned technology. Environmental perception technology is one of the core technologies of unmanned cars, environmental perception information comprehensiveness and accuracy is the guarantee of safe driving of unmanned cars, this paper elaborates on image recognition, sensor layout, sensor perception range and accuracy, sensor anti-interference ability and rapid processing of sensor massive data in environmental perception technology.

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References

Zhu Bing, Zhang Peixing, Zhao Jian, et al. Research progress of scenario-based virtual testing of autonomous vehicles [J]. China Journal of Highway and Transportation, 2019, 32(6): 1-19.

Peng Yuhui, Jiang Ming, Ma Zhongyuan, et al. Research progress of key technologies of automotive autonomous driving [J]. Journal of Fuzhou University, 2021, 49(5).

Wei Shichuan. New application of UAV inspection in overhead transmission lines [J]. Water Conservancy and Electric Power Technology and Application, 2022, 4(12): 22-25.

Li Keqiang, Dai Yifan, Li Shengbo, et al. Development status and trend of Intelligent Connected Vehicle (ICV) technology [J]. Journal of Automotive Safety and Energy Efficiency, 2017, 8 (01): 1.

Doll C N H, Muller J P, Morley J G. Mapping regional economic activity from night-time light satellite imagery[J]. Ecological Economics, 2006, 57(1): 75-92.

Martin B R. Foresight in science and technology[J]. Technology analysis & strategic management, 1995, 7(2): 139-168.

Kerrouche A, Leighton J, Boyle W J O, et al. Strain measurement on a rail bridge loaded to failure using a fiber Bragg grating-based distributed sensor system[J]. IEEE sensors Journal, 2008, 8(12): 2059-2065.

Burkacky O, Deichmann J, Doll G, et al. Rethinking car software and electronics architecture[J]. McKinsey & Company, 2018: 11.

Lindtner S. Hackerspaces and the Internet of Things in China: How makers are reinventing industrial production, innovation, and the self[J]. China Information, 2014, 28(2): 145-167.

Chiu Y, Huang C, Chen Y C. The R&D value-chain efficiency measurement for high-tech industries in China[J]. Asia Pacific Journal of Management, 2012, 29: 989-1006.

Ahmedov I. The impact of digital economy on international trade [J]. European Journal of Business and Management Research, 2020, 5(4).

Zhang N, Lior N, Jin H. The energy situation and its sustainable development strategy in China[J]. Energy, 2011, 36(6): 3639-3649.

Lo K, Wang M Y. Energy conservation in China’s Twelfth Five-Year Plan period: continuation or paradigm shift?[J]. Renewable and Sustainable Energy Reviews, 2013, 18: 499-507.

Chen Z, Li B, Chai X, et al. The overall development strategy research of “Internet Plus” Action Plan[J]. Strategic Study of Chinese Academy of Engineering, 2018, 20(2): 1-8.

Duan X, Jiang H, Tian D, et al. V2I based environment perception for autonomous vehicles at intersections[J]. China Communications, 2021, 18(7): 1-12.

Labayrade R, Aubert D, Tarel J P. Real time obstacle detection in stereovision on non flat road geometry through" v-disparity" representation[C]//Intelligent Vehicle Symposium, 2002. IEEE. IEEE, 2002, 2: 646-651.

Li Q, Zheng N, Cheng H. Springrobot: A prototype autonomous vehicle and its algorithms for lane detection[J]. IEEE transactions on intelligent transportation systems, 2004, 5(4): 300-308.

Lai A H S, Yung N H C. Lane detection by orientation and length discrimination[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2000, 30(4): 539-548.

Xu L, Oja E, Kultanen P. A new curve detection method: randomized Hough transform (RHT)[J]. Pattern recognition letters, 1990, 11(5): 331-338.

Rosenfeld A, Thurston M. Edge and curve detection for visual scene analysis[J]. IEEE Transactions on computers, 1971, 100(5): 562-569.

Parent P, Zucker S W. Trace inference, curvature consistency, and curve detection[J]. IEEE Transactions on pattern analysis and machine intelligence, 1989, 11(8): 823-839.

Chen Z, Chen K, Lin W, et al. Piou loss: Towards accurate oriented object detection in complex environments [C]// Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. Springer International Publishing, 2020: 195-211.

Abdel-Mottaleb M, Elgammal A. Face detection in complex environments from color images[C]//Proceedings 1999 International Conference on Image Processing (Cat. 99CH363 48). IEEE, 1999, 3: 622-626.

Abdel-Mottaleb M, Elgammal A. Face detection in complex environments from color images[C]//Proceedings 1999 International Conference on Image Processing (Cat. 99CH36 348). IEEE, 1999, 3: 622-626.

Shang E, An X, Li J, et al. Robust unstructured road detection: The importance of contextual information[J]. International Journal of Advanced Robotic Systems, 2013, 10(3): 179.

Mayeur A, Bremond R, Bastien J M C. The effect of the driving activity on target detection as a function of the visibility level: Implications for road lighting[J]. Transportation research part F: traffic psychology and behaviour, 2010, 13(2): 115-128.

Dollar P, Wojek C, Schiele B, et al. Pedestrian detection: An evaluation of the state of the art[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 34(4): 743-761.

Wang Z R, Jia Y L, Huang H, et al. Pedestrian detection using boosted HOG features[C]//2008 11th International IEEE conference on intelligent transportation systems. IEEE, 2008: 1155-1160.

Premebida C, Ludwig O, Nunes U. LIDAR and vision‐based pedestrian detection system[J]. Journal of Field Robotics, 2009, 26(9): 696-711.

Labayrade R, Aubert D, Tarel J P. Real time obstacle detection in stereovision on non flat road geometry through" v-disparity" representation[C]//Intelligent Vehicle Symposium, 2002. IEEE. IEEE, 2002, 2: 646-651.

Arunmozhi A, Park J. Comparison of HOG, LBP and Haar-like features for on-road vehicle detection[C]//2018 IEEE International Conference on Electro/Information Technology (EIT). IEEE, 2018: 0362-0367.

Omachi M, Omachi S. Traffic light detection with color and edge information[C]//2009 2nd IEEE International Conference on Computer Science and Information Technology. IEEE, 2009: 284-287.

Fairfield N, Urmson C. Traffic light mapping and detection [C] //2011 IEEE International Conference on Robotics and Automation. IEEE, 2011: 5421-5426.

De La Escalera A, Moreno L E, Salichs M A, et al. Road traffic sign detection and classification[J]. IEEE transactions on industrial electronics, 1997, 44(6): 848-859.

Houben S, Stallkamp J, Salmen J, et al. Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark[C]//The 2013 international joint conference on neural networks (IJCNN). Ieee, 2013: 1-8.

Jiang Y, Zhou S, Jiang Y, et al. Traffic sign recognition using ridge regression and Otsu method[C]//2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2011: 613-618.

Ostachowicz W, Soman R, Malinowski P. Optimization of sensor placement for structural health monitoring: A review[J]. Structural Health Monitoring, 2019, 18(3): 963-988.

Rosique F, Navarro P J, Fernández C, et al. A systematic review of perception system and simulators for autonomous vehicles research[J]. Sensors, 2019, 19(3): 648.

Zhong Lingzhi, Liu Liping, GE Runsheng. Research status and prospect of millimeter wave cloud detection radar [J]. Advance in Earth Science, 2009, 24(4): 383.

Jourdan D B, de Weck O L. Layout optimization for a wireless sensor network using a multi-objective genetic algorithm [C]// 2004 IEEE 59th Vehicular Technology Conference. VTC 2004 -Spring (IEEE Cat. No. 04CH37514). IEEE, 2004, 5: 2466-2470.

Zhang Y, Xia L, Zhou C, et al. Microstructured fiber based plasmonic index sensor with optimized accuracy and calibration relation in large dynamic range[J]. Optics Communications, 2011, 284(18): 4161-4166.

Zhai C, Luo Z, Song X, et al. A superior selective and anti-jamming performance triethylamine sensing sensor based on hierarchical WO3 nanoclusters[J]. Journal of Alloys and Compounds, 2021, 857: 157545.

Sensor technology and experiment: Sensor configuration, calibration and experiment [M]. Tsinghua University Press Co., LTD. 2005.

Kohlert M, König A. Large, high-dimensional, heterogeneous multi-sensor data analysis approach for process yield optimization in polymer film industry[J]. Neural Computing and Applications, 2015, 26: 581-588.

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Published

26-06-2023

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Articles

How to Cite

Song, Z., & Deng, H. (2023). Research on Sensor Optimization Technology of Driverless Vehicle. Frontiers in Computing and Intelligent Systems, 4(2), 131-137. https://doi.org/10.54097/fcis.v4i2.10370