基于心理能量思想的人工情感模型

从人工情感建模的需要出发,根据动力心理学关于心理能量的理论,提出了情感能量的概念以及基于情感能量的情感状态的数学描述方法,建立了情感状态的能量分布描述空间和情感状态的概率描述空间。在此基础上,进一步分析了情绪状态的变化过程,并提出了情绪状态自发转移过程的马尔可夫链模型以及情绪状态刺激转移过程的隐马尔可夫模型。     

家庭服务机器人情感计算模型的研究

根据基本情感理论建立了家庭服务机器人的情感状态概率空间模型,并应用马尔可夫链的特性,建立了基于隐马尔可夫模型的情感计算模型.详细地阐述了该情感计算模型中各参数的意义以及估算方法.通过仿真实验验证了该情感计算模型可以较好地模拟情感状态的自发转移,以及在外部刺激作用下的情感转移.通过对实验数据分析,发现机器人的情感经外部刺激作用或者自发演变,最终趋于稳定状态,这个稳定状态与情感转移概率矩阵有关,而与机器人所处的初始情感状态无关.     

人工心理情感模型的个人机器人

以心理动力学中心理能量概念为基础,根据情感能量守恒定律,建立了情感状态能量分布描述空间和情感状态的概率描述空间,分析情绪状态的变化过程,提出情绪状态自发转移过程的隐马尔可夫链及其模型算法.利用MATLAB建立相关情感状态变化的仿真研究平台,研究情感状态的变化规律.根据以上人工心理情感模型及其变化规律构建出个人机器人综合研究平台软、硬件体系结构,并通过该系统的实际运行实验验证了其有效性.     

基于状态空间与概率空间映射的极大相似度匹配情感模型

机器人情感建模是研究情感机器人的热点问题。文中以情感心理学知识为基础,模拟具有不同个性的情感机器人在外界刺激作用下情感动态变化的过程,研究个性和外界刺激对情感转移过程的影响。采用基于状态空间的情感空间模型来描述机器人的情感状态,并用HMM过程来模拟情感状态的转移过程。但HMM过程只能求得当前情感状态的概率,为得到具体的情感状态,文中提出一种基于状态空间与概率空间映射的极大相似度匹配的情感转移模型。首先利用HMM过程计算出当前情感概率,然后通过极大相似度匹配来得到转移后具体的情感状态。通过调节模型参数来模拟不同个性和外界刺激,该模型能有效模拟情感状态变化过程。实验结果验证模型模拟的情感变化过程符合人类情感变化的一般规律。     

人机交互中的个性化情感模型

人与机器人的交互过程中,情感因素的引入能够使人机交流更加自然和谐.因此,完整的人工情感模型的建立是首要解决的问题.基于情感能量理论基础,首先,提出了心境自发转移和刺激转移模型.其次,结合情绪自发转移的马尔可夫链模型和刺激转移的HMM模型,将心境和情绪的自发和刺激转移过程统一在一个框架下.最后,将完整的人工情感模型软件化并应用于儿童玩伴机器人上,在接受非结构化环境与用户的信息输入后,个性化的情感软件模块产生输出,实现针对儿童用户的玩伴机器人个性化交互,通过应用验证了该模型的有效性.     

基于马尔可夫链的情感计算建模方法

定义了情感的两种状态及其两个基本的变化转移过程,并应用马尔可夫链构造了一个情感概率空间,建立模拟情感变化的情感模型,给出了情感能量、情感强度和情感熵等概念,用以描述情感特征与情感状态。通过Matlab的仿真计算,验证此模型可较好地模拟情感状态自发转移的动态过程,可用于情感机器人的情感模拟计算。为情感计算和情感自动生成理论研究提供了一种新途径。     

情感机器人的情感模型研究

本文建立了仿人类情感的情感模型,在情感模型中建立了三维情感空间,采用马尔可夫过程来描述情感状态的变化转移过程,并提出了性格矩阵,对比分析了不同性格人对相同刺激的反应.应用D-S证据理论融合来自视觉、声音及其他方面的情感信息,分析两种因素同时作用时引起情感状态的转移规律.最后将情感模型应用于情感机器人系统,使机器人可以根据外界刺激产生情感,并做出相应的表情.实验结果证明了情感模型的有效性.     

隐马尔可夫情感模型的复合情绪生成

情感模型的建立,是产生人工情感的一种途径,也是和谐人机交互的基础.针对现有的隐马尔可夫情感模型仅能产生基本情绪的问题,提出了一种改进的模型,使其能够产生复合情绪.首先,放宽已有理论的假设条件,使得某种刺激可以引发多种基本情绪,并且2种基本情绪状态的强度可以同时增大,提高了模型的普适性;其次在引入辅助矩阵和可变阈值后,实现了情感模型的复合情绪生成.通过仿真试验,验证了该模型的有效性.     

一种基于状态空间分析法的人工情感模型

从人类情感的特征出发,分析了情感的变化规律,引入转移系数矩阵、情感强度的衰减因子以及情感的灵敏因子描述不同个体的情感特征,提出一种基于马尔可夫链和状态空间分析方法的情感模型构建方法,分析了该模型的稳定性,并使用Matlab软件对情感的自发转移和控制输入时的情感变化以及无输入时情感的变化趋势进行了仿真,仿真结果说明了模型所表示的情感过程接近于人类的情感过程。     

基于非线性状态空间模型的情感模型研究

如何赋予机器情感智力以实现和谐自然的人机交互是人工情感研究的核心内容,其中情感建模是研究重点和难点.基于心理学的基本情绪论和人格特质论提出了一种非线性情感模型.在模型中首先定义了情绪空间、心境空间和个性空间,确定了心境、个性对情绪的影响关系矩阵;其次采用非线性系统的状态空间模型来模拟人类情感状态的变化;最后对模型进行了仿真,仿真结果表明该模型体现了人类情绪的非线性特点,符合人类情感变化规律.     

基于混合模糊多人多目标非合作博弈的车道选择模型

建立汽车安全驾驶辅助系统（包括安全驾驶预警系统）是保证交通安全的有效手段.准确预测车辆集群态势是汽车安全辅助驾驶的前提,车道选择是车辆集群态势发生转移最为根本的原因,也是交通流理论研究的基本内容.以往研究没有综合考虑车辆集群复杂态势下各运动实体特征及其操控者类型,以及多个车道间车辆的冲突对车道选择的影响.为此,本文综合考虑各运动实体特征及其操控者类型,基于混合模糊多人多目标非合作博弈方法,建立城市快速路基本路段上的驾驶员车道选择模型.通过分析各方驾驶员在不同车道选择策略下的收益,确定换道博弈的Nash均衡,得到驾驶员最优车道选择策略.研究结果表明:基于混合模糊多人多目标非合作博弈方法建构的驾驶员车道选择模型,其预测准确率可达到85.2%.     

Shared control for lane departure prevention based on the safe envelope of steering wheel angle

The ability to prevent lane departure has become an important feature for commercialized vehicles. This paper proposes a shared steering assistance strategy based on a safe envelope of steering wheel angle (SWA). This solves the human-machine conflict issue in lane departure prevention (LDP) system which uses steering control to help the driver keep the vehicle within the correct lane. The system combines a driver steering control model, current vehicle states and vehicle-road deviation. The desired SWAs are calculated when the driver intends to drive along the left or right side of the lane, and then the two angles are used to generate the safe envelope. Next, a driver intention estimator is designed to predict driver’s intended SWA and the assistance control is activated by judging whether the driver intended SWA is go beyond the safe envelope. Finally, a H∞ controller and a disturbance observer are developed to determine the assistance torque. In this way, the SWA is limited to safe values to mitigate lane departure and the controller intervention is minimized. The effectiveness of the proposed method is evaluated via numerical simulation with different driving scenarios and human-in-the-loop experiment on a driving simulator. The obtained results show that this method not only can avoid lane departures effectively, but also ensures a good human-machine cooperative performance.     

Effects of single versus multiple warnings on driver performance

OBJECTIVE: To explore how a single master alarm system affects drivers' responses when compared with multiple, distinct warnings. BACKGROUND: Advanced driver warning systems are intended to improve safety, yet inappropriate integration may increase the complexity of driving, especially in high workload situations. This study investigated the effects of auditory alarm scheme, reliability, and collision event type on driver performance. METHOD: Using a 2 x 2 x 4 mixed factorial design, we investigated the impact of two alarm schemes (master vs. individual) and two levels of alarm reliability (high and low) on distracted drivers' performance across four collision event types (frontal collision warnings, left and right lane departure warnings, and warnings for a fast-approaching following vehicle). RESULTS: Participants' reaction times and accuracy rates were significantly affected by the type of collision event and alarm reliability. The use of individual alarms, rather than a single master alarm, did not significantly affect driving performance in terms of reaction time or response accuracy. CONCLUSION: Even though a master alarm is a relatively uninformative warning, it produced statistically no different reaction times or accuracy results when compared with information-rich auditory icons, some of which were spatially located. In addition, unreliable alarms negatively impacted driver performance, regardless of event type or alarm scheme. APPLICATION: These results have important implications for the development and implementation of multiple driver warning systems.     

基于机器视觉的轻量级驾驶辅助系统

为了解决计算机视觉应用中数据量大、算法复杂的问题,根据道路结构特征和车辆行为特征,采用单个摄像头作为传感器,实现了一种轻量级的安全辅助驾驶系统。首先采用改进的边缘提取算法和车道线检测算法对摄像机内外参数进行离线标定;接着根据标定结果在二维平面图像上采用标识出实际空间距离的多窗口划分方法,并按不同的车间距将不同窗口划分为不同安全系数的区域,以赋予道路视觉检测的几何先验知识;当区域中出现障碍物时发出相应警示信息进行安全驾驶辅助,能为智能辅助驾驶提供轻量级的视觉检测平台。以便携式计算机和固定在车内的摄像头作为实验装置,在城市道路上进行车载实验。系统在车载实验中能够快速地提取车辆两侧的车道线,并利用离线标定的结果快速生成不同安全系数的警示区域,其中车辆在车道内正常行驶时的误检率和漏检率很小,可以忽略不计。与传统的驾驶辅助系统相比,本系统计算量大大降低,检测流程得到简化,可实现轻量级的车道和车辆检测,为系统在嵌入式系统上的实现奠定基础。     

Shared steering control: How strong and how prompt should the intervention be for a better driving experience?

The lane keeping assistance system, a representative advanced driver assistance system, comprises a shared control that cooperates with the driver to achieve a common goal. The steering experience of the driver may vary significantly depending on the auto-steering control strategy of the system. In this study, we examined the driving experience with various steering control strategies. Nine control strategies (three torque amounts × three deviations in starting control) were established as prototypes. Eighteen drivers participated in the evaluation of each strategy in a highway environment on a driving simulator. A two-way repeated measure ANOVA was used to assess the effects of the system. Both the objective measures (standard deviation of lane position, steering reversal rate, and root mean square of lateral speed) and subjective measures (pleasure and arousal of emotion, trust, disturbance, and satisfaction) were evaluated and analyzed. The results showed that a torque amount of 3 Nm evoked feelings of high disturbance and negative emotional responses. A deviation in starting control (DEV) of 0.80 m yielded unstable lane keeping performances and evoked negative effects on pleasure, trust, and satisfaction. A regression model for the driver satisfaction recommended a torque of 2.32 Nm and a DEV of 0.27 m as the optimal design parameters. This proposed strategy is expected to improve the experience design of lateral semi-autonomous vehicles.     

Identification of aggressive driving behavior of online car-hailing drivers based on association classification

With the rapid development of online car-hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10-fold cross-test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule-based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty-six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U-turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.     

A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

Joint road geometry estimation and vehicle tracking

Detection and tracking of other vehicles and estimation of lane geometry will be required for many intelligent driver assistance systems in the future. By combining the processing of these two features into a single filter, better utilisation of the available information can be achieved. For instance, it is demonstrated that it is possible to improve the road shape estimate by including information about the lateral movement of leading vehicles.

Statistical evaluation is done by comparing the estimated parameters to true values in varying road and weather conditions. The performance is also related to typical requirements of active safety applications such as adaptive cruise control and a new safety function called emergency lane assist.