System for Adapting Vehicle dynamic parameters to the driving Environment and Driver capabilities
Over the past 5 to 8 years, the number of accidents and fatalities has become constant, or even slightly increasing, following a 10 to 15 year period of a steady decrease of those indicators in countries with long road safety tradition. This suggested that the traditional road safety measures, including existing intelligent vehicle measures might have reached their limit of capability of significantly decreasing the number and severity of accidents. In Portugal, despite the rapid mobilization, the number of road accidents has lately decreased considerably. It is, however, expected that the same pattern as in the more developed countries is going to be repeated on a shorter timescale. Human factors contribute to over 95% of the road accidents and the driving task does not continually present the same degree of difficulty. SAVED, in the area of Intelligent Transportation Systems, sought to employ innovative methods to provide a lasting significant improvement to road safety.
The main aim of this study was to contribute to the improvement of road safety through the application of Intelligent Vehicle Combined Passive and Active Safety Systems. Its main objective was to recommend a system to adjust the vehicle's dynamic attributes to the driver's state and driving circumstances, which will ultimately grow into a technological device. As such, this work proposed to contribute to developing the specifications of a three-unit (Sensor Module, Risk Profiling System, and Control Tool) in-vehicle technological system which will continuously assess drivers' competencies and surrounding conditions, and automatically adjust the action space of the driver to preserve the desired safety levels.
Four initial objectives have therefore been identified:
- to list drivers' (permanent and temporary) limitations and driving circumstances which have an impact on road accident involvement;
- to develop a procedure to identify which aspects of risk perception and vehicle control could be affected by each of the limiting factors;
- to specify information or vehicle dynamics adjustments for those limitations. If amendment for one or more limitations is not possible or does not substantially reduce the probability and severity of accidents, to identify a list of restrictions of the driving task in one or more dimensions (e.g. speed, longitudinal and lateral acceleration, and distance to the vehicle ahead);
- to develop a business model.
The methodology used encompassed the design and development of a three-unit tool, which required:
- analysis of hazards related to Human Factors Driving Environment - Vehicle Attributes (Module A);
- establishment of multi-dimensional risk profile (Module B);
- concept and specification of control tool (Module C);
- development of a business model for its deployment Human Factors Driving Environment Vehicle Attributes risk profile control tool business model
An initial computer model for about 500 meters of a non-urban highway and 80 cars driven have been developed using Agents in a MultiAgent-Based program named AnyLogic. This will be scaled up by using the outputs of this sort of models in traffic programs, like AIMSUN.
Project impacts include:
- to enhance Road Safety integrated with other policies – ex. environmental;
- to reduce Hazards associated with Human Factors (responsible for 95% of accidents) continuously and automatically;
- to enhance an Agent-Based traffic flow analysis model suitable for road safety studies;
- to design a deployment map - the role of Specific Driver Groups;
- to extend and complement MIT Portugal SCUSSE and Citymotion research projects.
Institutions/Research Centers involved:
- Instituto Superior Técnico;
- Faculdade de Ciências e Tecnologia da Universidade de Coimbra (FCT/UC);
- Universidade do Minho