Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Comparing the self-assessed and examiner-assessed driving skills of Japanese driving school students

Previous research about subjective driving skills has revealed that drivers, especially younger males, tend to rate their driving skills more highly than those of the average driver. This study examines the accuracy of Japanese novice drivers' self-evaluation of their driving skills by comparing their self-assessments with assessments made by a driving examiner. We also examined the effects of gender and age on the accuracy of driver self-evaluation of their skills.     

具有随机道路输入的人—车—路闭环操纵系统的响应分析与操纵安全性评价

本文建立了一种平衡随机道路输入的数学模型。应用精细积分法，进行了具有此类随机道路输入的人－车－路闭环操纵系统响应的仿真研究与汽车的主动安全性评价。该方法不仅为精确算法，珂能缩短汽车主动安全性的评价周期，避免实车试验带来的巨大的经济负担。结合实例，说明该方法是正确的，其结果是有效而合理的。     

人-车-路闭环系统驾驶员模型参数辨识

基于在驾驶模拟器上进行的人－车－路闭环系统仿真试验，使用全局演化局部寻优的辨识算法，对驾驶员模型进行参数辨识。通过大量的实际驾驶人员的试验数据，辨识出各种水平的驾驶员模型参数，为改进智能车控制系统的设计提供了依据。利用辨识得到的驾驶员模型和车辆模型进行了人－车－路闭环系统的双移线与蛇行线仿真，仿真结果与试验数据具有很好的一致性。     

用神经网络仿真驾驶员转向特性的研究

在人－车－环境综合系统中，由于驾驶员操作因素的不确定性和非线性，政难以建立精确模型。通过使用神经网络的方法对驾驶员的转向操作进行了模拟和仿真，取得了满意的结果，为更加精确定量地分析驾驶员的转向操作特性提供了一条新的途径。     

Modelling supported driving as an optimal control cycle: Framework and model characteristics

Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper puts forward a receding horizon control framework to model driver assistance and cooperative systems. The accelerations of automated vehicles are controlled to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller design of Adaptive Cruise Control (ACC) and Cooperative ACC (C-ACC) systems. The proposed ACC and C-ACC model characteristics are investigated analytically, with focus on equilibrium solutions and stability properties. The proposed ACC model produces plausible human car-following behaviour and is unconditionally locally stable. By careful tuning of parameters, the ACC model generates similar stability characteristics as human driver models. The proposed C-ACC model results in convective downstream and absolute string instability, but not convective upstream string instability observed in human-driven traffic and in the ACC model. The control framework and analytical results provide insights into the influences of ACC and C-ACC systems on traffic flow operations.     

基于Python的驾驶员打哈欠在线检测方法研究

驾驶疲劳是造成交通事故的主要诱因之一。由世界卫生组织发布的调查研究结果表明,在高速公路中由疲劳驾驶引起的交通事故率占了将近百分之四十,驾驶疲劳检测和预警的技术也越来越受到研究人员的重视。而驾驶员打哈欠是疲劳形成过程中的一个直观指标,文章提出基于Python平台开发一套在线检测驾驶员打哈欠的系统,方法简单、指标稳定可靠,具有实际应用价值。