01099477

Component

In this work different approaches regarding simulation of vehicle longitudinal movement in car-following regime have been tested in order to develop an Adaptive Cruise Control (ACC) system with human-like driving capabilities. The main idea is to develop an ACC system that can be continuously trained by drivers to accommodate their actual driving preferences as these change among drivers and over time. In particular, this work is a preliminary study on a learning machine capable of learning and memorizing the driving attitude of a given driver and reproducing it automatically. The simulation of driving behavior and the identification of a driving desired trajectory have been the object of many studies, which led to the formulations of several longitudinal movement models, known as "car-following models". The Gipps car-following model has been used in this work to test and to compare the performances of different approaches, based on simple regression models (linear and polynomial) and complex regression models (artificial neural networks). The comparisons among different models will be based on accurate experimental data consisting in long trajectories of vehicle platoons gathered on urban roads.

01084478

08-2451

English

16p

2008

Transportation Research Board 87th Annual Meeting

DVD

Figures (2) ; References (14) ; Tables (5)

Advanced vehicle control systems; Automobile drivers; Autonomous intelligent cruise control; Car following; Neural networks; Traffic characteristics; Traffic platooning; Traffic simulation; Travel behavior; Travel surveys; Vehicle spacing

Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Society; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2008 Paper #08-2451

TRIS, TRB

Jan 29 2008 4:52PM