面向行人防碰撞预警的驾驶员驾驶意图辨识方法研究
发布时间：2018-08-06 19:56
【摘要】：从十九世纪到二十一世纪,汽车已经发展成为交通运输的中坚力量。与此同时,汽车工业的发展也造成了严重的环境和交通安全问题。人们对于交通安全问题的研究也从单纯提高车辆自身的安全技术水平逐步转移到综合考虑车辆、驾驶员以及环境等各个因素来提高道路交通安全。在道路交通事故中,车辆和行人的碰撞是最主要的事故形式之一,而行人是事故中最大的受害群体。因此,如何提高汽车的主动安全性能、有效保护行人的安全已经越来越被重视。在国家自然科学基金项目(61104165)和中央高校基本科研业务费专项资金资助项目(DUT13JS02)的资助下,本文开展了面向行人防碰撞预警的驾驶员驾驶意图辨识方法的研究。在检测到车辆前方存在行人的基础上,本文确定了需要辨识的4种驾驶意图,即加速、减速制动、转弯避让(正常转向)和急转。通过分析驾驶意图产生的机理和统计模式识别理论,确定应用隐马尔科夫模型(Hidden Markov Model, HMM)来辨识驾驶员驾驶意图。根据需要辨识的4种驾驶员意图,通过驾驶模拟器采集实验所需的传感器数据,并对实验数据进行处理。采用罗马诺夫斯基准则对传感器数据的异常值进行剔除,使用改进的K-means算法对驾驶员正常转向和紧急转向的界限值进行确定。根据隐马尔科夫HMM模型的Baum-Welch算法和前向算法,使用MATLAB结合Baum-Welch算法和前向算法编写m文件进行驾驶员意图辨识。使用Baum-Welch算法进行驾驶意图隐马尔科夫HMM模型的离线训练。由于不同模型间观察序列的长度不同,为了使实验得到的模型更加精确,对Baum-Welch算法进行了改进。训练得到表征驾驶意图隐马尔科夫HMM模型的参数。最后,使用处理后的观察序列进行驾驶员意图在线辨识。将观察序列输入到搭建好的隐马尔科夫HMM模型中,用MATLAB结合前向算法编写m文件得到观察序列和不同模型间的匹配值,匹配值最大的模型视为驾驶员意图隐马尔科夫HMM模型。基于车辆前方行人检测结果,在辨识得到驾驶员意图后,进行行人防碰撞预警机制的确定。对错误的驾驶员操作,如误踩加速踏板,通过制定的对驾驶员和行人同时预警的机制,可以有效保护行人安全。
[Abstract]:From the nineteenth century to the 21 century, the automobile has developed into the backbone of transportation. At the same time, the development of automobile industry has also caused serious environmental and traffic safety problems. The research on traffic safety has been gradually transferred from simply improving the safety technology level of vehicles to taking into account various factors such as vehicles drivers and environment to improve road traffic safety. In road traffic accidents, the collision between vehicles and pedestrians is one of the most important accident forms, and pedestrians are the largest victims of accidents. Therefore, more and more attention has been paid to how to improve the active safety of vehicles and effectively protect the safety of pedestrians. With the support of the National Natural Science Foundation of China (61104165) and the DUT13JS02 (Central University basic Scientific Research Business Fund) project, this paper studies the identification method of driver's driving intention for pedestrian anti-collision warning. On the basis of detecting the presence of pedestrians in front of the vehicle, this paper determines four driving intentions that need to be identified, that is, acceleration, deceleration and braking, turning and avoiding (normal steering) and sharp turning. By analyzing the mechanism of driving intention and the theory of statistical pattern recognition, the hidden Markov model (Hidden Markov Model, HMM) is used to identify driver's driving intention. According to the four kinds of driver's intention which need to be identified, the sensor data needed in the experiment are collected by driving simulator, and the experimental data are processed. The outliers of sensor data are eliminated by Romonovsky criterion, and the limit values of normal steering and emergency steering of drivers are determined by the improved K-means algorithm. According to the Baum-Welch algorithm and forward algorithm of Hidden Markov HMM model, the author uses MATLAB combined with Baum-Welch algorithm and forward algorithm to write m file for driver intention identification. The Baum-Welch algorithm is used to train the hidden Markov HMM model of driving intention. In order to make the model more accurate, the Baum-Welch algorithm is improved because of the different length of observation sequence among different models. The parameters of the hidden Markov HMM model are obtained. Finally, the treated observation sequence is used to identify the driver's intention online. The observation sequence is input into the constructed hidden Markov HMM model, and the m file is compiled by MATLAB combined with the forward algorithm to get the matching value between the observation sequence and the different models. The model with the largest matching value is regarded as the hidden Markov model of the driver's intention. Based on the results of pedestrian detection in front of the vehicle, the pedestrian collision prevention warning mechanism is determined after the driver's intention is identified. The safety of pedestrians can be effectively protected by the wrong driver operation such as stepping on the accelerator pedal by establishing a warning mechanism for both the driver and the pedestrian.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U463.6;U495
本文编号：2168823
[Abstract]:From the nineteenth century to the 21 century, the automobile has developed into the backbone of transportation. At the same time, the development of automobile industry has also caused serious environmental and traffic safety problems. The research on traffic safety has been gradually transferred from simply improving the safety technology level of vehicles to taking into account various factors such as vehicles drivers and environment to improve road traffic safety. In road traffic accidents, the collision between vehicles and pedestrians is one of the most important accident forms, and pedestrians are the largest victims of accidents. Therefore, more and more attention has been paid to how to improve the active safety of vehicles and effectively protect the safety of pedestrians. With the support of the National Natural Science Foundation of China (61104165) and the DUT13JS02 (Central University basic Scientific Research Business Fund) project, this paper studies the identification method of driver's driving intention for pedestrian anti-collision warning. On the basis of detecting the presence of pedestrians in front of the vehicle, this paper determines four driving intentions that need to be identified, that is, acceleration, deceleration and braking, turning and avoiding (normal steering) and sharp turning. By analyzing the mechanism of driving intention and the theory of statistical pattern recognition, the hidden Markov model (Hidden Markov Model, HMM) is used to identify driver's driving intention. According to the four kinds of driver's intention which need to be identified, the sensor data needed in the experiment are collected by driving simulator, and the experimental data are processed. The outliers of sensor data are eliminated by Romonovsky criterion, and the limit values of normal steering and emergency steering of drivers are determined by the improved K-means algorithm. According to the Baum-Welch algorithm and forward algorithm of Hidden Markov HMM model, the author uses MATLAB combined with Baum-Welch algorithm and forward algorithm to write m file for driver intention identification. The Baum-Welch algorithm is used to train the hidden Markov HMM model of driving intention. In order to make the model more accurate, the Baum-Welch algorithm is improved because of the different length of observation sequence among different models. The parameters of the hidden Markov HMM model are obtained. Finally, the treated observation sequence is used to identify the driver's intention online. The observation sequence is input into the constructed hidden Markov HMM model, and the m file is compiled by MATLAB combined with the forward algorithm to get the matching value between the observation sequence and the different models. The model with the largest matching value is regarded as the hidden Markov model of the driver's intention. Based on the results of pedestrian detection in front of the vehicle, the pedestrian collision prevention warning mechanism is determined after the driver's intention is identified. The safety of pedestrians can be effectively protected by the wrong driver operation such as stepping on the accelerator pedal by establishing a warning mechanism for both the driver and the pedestrian.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U463.6;U495
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