Stochastic Modelling and Analysis of Driver Behaviour

Driver behaviour is considered a key factor in the majority of car accidents. As a consequence driver behaviour has been receiving vast attention in different domain areas, such as psychology, transport engineering and computer science. Computer scientists are primarily interested in what and how computing means can be applied to understand the relation between driver behaviour and transport systems. In this paper, we adopt a stochastic approach to conduct a quantitative investigation of driver behaviour. We use the Markovian process algebra PEPA (Performance Evaluation Process Algebra) to describe the overall system model. The system component describing the topology and dynamic of the traffic is composed in parallel with the system component describing the driver state and its evolution due to experience. We illustrate our approach using a three-way junction as an example and present the numerical results of the system analysis

Reducing fuel consumption by using a new fuel-efficiency support tool

A fuel-efficiency support tool has been designed, which includes a normative model describing optimal driver behaviour for minimising fuel consumption. If actual behaviour deviates from optimal behaviour, the system presents advice on how to change behaviour. Evaluation revealed that drivers used ~16% less fuel compared with `normal driving

An agent-based traffic simulation framework to model intelligent virtual driver behaviour

This paper presents an agent-based traffic simulation framework that supports intelligent virtual driver behaviour. The framework exploits concepts used in Artificial Life (ALife), Artificial Intelligence (AI) and Agent technology to model the inherent unpredictability and autonomous behaviour of drivers within traffic simulation models. Each driver agent in our system contains knowledge and a decision-making mechanism, both of which are based on heuristics. This approach replaces some of the prescriptive nature of driving simulation models by allowing behaviours to emerge as a result of individual driver agent interactions. The framework also contributes to accident analysis by improving current limitations in which accident investigation methods concentrate on the events themselves, rather than pre-crash influences. Within this context, the framework provides an opportunity to increase the understanding of accident causation factors, to examine alternative traffic scenarios (what if analyses) and methodology to obtain quantitative estimates of accident risk. Current implementation results show that driver agents within the integrated simulation are able to perceive other drivers’ speeds and distances, avoid collisions, perform realistic vehicle following, and demonstrate emergent traffic flow. A major application area for this framework includes the evaluation of vehicle, highway and road user factors that precede a collision, or near misses

Future information and assistance systems for train drivers and evaluation of their usability

Even though train protection systems are used to avoid critical situations, the train driver remains responsible for the continuous monitoring of signal aspects and derivation of suitable actions. This requirement persists, although the position of signals shifts more and more from external signals to in-cab displays, especially with advanced levels of train protection and automatic train control. Errors in the detection and interpretation of signal- or display information and driver distraction may lead to severe accidents. Aim of our research at the Institute of Transportation Systems (ITS) is to develop innovative concepts of the train driver’s workplace in order to secure a safe and efficient railway system that keeps the driver ‘in the loop’. Therefore, we follow a user centred approach. The train driver participates directly in the development and evaluation process of new systems supporting the work in the driver’s cabin. Using our driver’s cabin simulator recently built at the ITS as a flexible vehicle platform in a simulation environment, we are able to investigate the driving behaviour and the train driver’s information processing during his or her task. From the results, we derive concepts in order to optimize the presentation of necessary information and give recommendations how to assist the train driver. In the present paper, first concepts for supporting the train driver in keeping attention and also our simulation environment and the methodology used are described

An adaptive and rule based driving system for energy-e cient and safe driving behaviour

Falta palabras claveSaving energy and protecting the environment became fundamental for society and politics, why several laws were enacted to increase the energye ciency. Furthermore, the growing number of vehicles and drivers leaded to more accidents and fatalities on the roads, why road safety became an important factor as well. Due to the increasing importance of energye ciency and safety, car manufacturers started to optimise the vehicle in terms of energy-e ciency and safety. However, energy-e ciency and road safety can be also increased by adapting the driving behaviour to the given driving situation. This thesis presents a concept of an adaptive and rule based driving system that tries to educate the driver in energy-e cient and safe driving by showing recommendations on time. Unlike existing driving systems, the presented driving system considers energy-e ciency and safety relevant driving rules, the individual driving behaviour and the driver condition. This allows to avoid the distraction of the driver and to increase the acceptance of the driving system, while improving the driving behaviour in terms of energy-e ciency and safety. A prototype of the driving system was developed and evaluated. The evaluation was done on a driving simulator using 42 test drivers, who tested the e_ect of the driving system on the driving behaviour and the e_ect of the adaptiveness of the driving system on the user acceptance. It has been proven during the evaluation that the energy-e ciency and safety can be increased, when the driving system was used. Furthermore, it has been proven that the user acceptance of the driving system increases when the adaptive feature was turned on. A high user acceptance of the driving system allows a steady usage of the driving system and, thus, a steady improvement of the driving behaviour in terms of energy-e ciency and safety

Piloting a telemetric data tracking system to assess post-training real driving performance of young novice drivers

Evaluating the effects of driver training interventions is a difficult research task. The ultimate goal of such interventions is to make the driver safer and therefore less likely to be involved in a road crash. A particular driver training intervention can only be considered to be effective if it can show a significant reduction in the number crashes for the driver, or a significant change in driver behaviour that clearly implies safer driving. Getting accurate and comprehensive crash records is difficult and to measure post training behavioural driving changes based on selfreports (e.g., log books) may not be accurate enough to be statistically meaningful