汽车驾驶行为是影响燃油消耗和安全驾驶的重要因素,驾驶行为识别是对汽车安全驾驶和节能进行优化的基础。该文针对汽车转向和换道行为,通过加装汽车转向盘转角传感器,结合车载总线通信技术获取汽车行驶状态信息,基于汽车转向运动学推导车辆行驶状态与汽车行驶轨迹之间的映射关系,进一步建立汽车行驶方向向量模型,提出以车身轴线转角和最大转向盘转角为特征量的支持向量机线性分类器,并运用Lagrange数乘法和二次规划算法求解该最优分类问题。通过设计实车实验验证了该方法的有效性。实验结果表明: 该方法识别汽车的转向与换道驾驶行为的准确度达98%以上。该技术可用于汽车行驶安全预警与控制系统, 提升行驶安全。
Abstract
Driving behavior plays an important role in fuel consumption and safe driving. Thus, driving behavior recognition can improve driving safety and optimize energy use. This study presents a steering and lane-changing behavior recognition system based on the vehicle status obtained from a steering wheel angle sensor. A support vector machine linear classifier is then used to analyze the vehicle body transfer angle and maximum steering angle given by a moving direction vector model. Lagrange number multiplication and quadratic programming are used in an optimal classifier for recognizing steering and lane-changing behavior. Real vehicle tests show that this methodology has 98% accuracy for steering and lane-changing behavior recognition. This system can be integrated into a warning and control system to improve driving safety.
关键词
驾驶行为 /
行驶方向向量 /
支持向量机 /
最优分类
Key words
driving behavior /
moving direction vector /
support vector machine /
optimal classification
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参考文献
[1] Ericsson E. Independent driving pattern factors and their influence on fuel-use and exhaust emission factors [J]. Transportation Research Part D: Transport and Environment, 2001, 6(5): 325-345.
[2] Voort M, Dougherty M S, Maarseveen M. A prototype fuel-efficiency support tool [J]. Transportation Research Part C: Emerging Technologies, 2001, 9(4): 279-296.
[3] MENG Xiaoning, Lee K K, XU Yangsheng. Human driving behavior recognition based on hidden Markov models [C]// IEEE International Conference on Robotics and Biomimetics. Piscataway, NJ, USA: IEEE, 2006: 274-279.
[4] 宗长富, 杨肖, 王畅, 等. 汽车转向时驾驶员驾驶意图辨识与行为预测 [J]. 吉林大学学报: 工学版, 2009, 39(1): 27-32.ZONG Changfu, YANG Xiao, WANG Chang, et al. Driving intentions identification and behaviors prediction in car lane change [J]. Journal of Jilin University: Engineering and Technology Edition, 2009, 39(1): 27-32. (in Chinese)
[5] 宗长富, 王畅, 何磊, 等. 基于双层隐式马尔科夫模型的驾驶意图辨识 [J]. 汽车工程, 2011(8): 701-706.ZONG Changfu, WANG Chang, HE Lei, et al. Driving intention recognition based on double-layer HMM [J]. Automotive Engineering, 2011(8): 701-706. (in Chinese)
[6] LIU Li, XU Guoqing, SONG Zhangjun. Driver lane changing behavior analysis based on parallel Bayesian networks [C]// 2010 Sixth International Conference on Natural Computation (ICNC). Piscataway, NJ, USA: IEEE, 2010, 3: 1232-1237.
[7] HE Xiaokai, HU Jiajun, LU Jialiang, et al. Driver lane changing behavior [C]// 2011 International Conference on Computer Science and Network Technology (ICCSNT). Piscataway, NJ, USA: IEEE, 2011, 4: 2385-2389.
[8] Imamura T, Takeuchi Y, ZHANG Zhong, et al. Estimation for grasp behavior of vehicle driver by using steering wheel sensor system [C]// 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Piscataway, NJ, USA: IEEE, 2012: 1515-1519.
[9] Diehm G, Maier S, Flad M, et al. An identification method for individual driver steering behaviour modelled by switched affine systems [C]// 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). Piscataway, NJ, USA: IEEE, 2013: 3547-3553.
[10] Shirazi M M, Rad A B. Detection of intoxicated drivers using online system identification of steering behavior [J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4): 1738 - 1747.
[11] Tsunai H, Maeda K, Hayashi R, et al. Development of steering behavior recognition method by using sensing data of drive recorder [C]// International Conference on Control, Automation and Systems. Piscataway, NJ, USA: IEEE, 2008: 301-306.
[12] FU Mengyin, SHEN Zhonghong, YANG Yi, et al. The recognition and applications of floating cars'driving behaviors [C]// 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC). Piscataway, NJ, USA: IEEE, 2011: 680-683.
基金
国家“八六三”高技术项目(2012AA111901);国家留学基金项目(201406215015);清华大学汽车安全与节能国家重点实验室开放基金项目(KF14142)
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