Abstract

Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mo stretchy="false""("/mml:mo""mml:mn fontstyle="italic""1"/mml:mn""mml:mo stretchy="false"")"/mml:mo""/mml:math" achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"""mml:mo stretchy="false""("/mml:mo""mml:mn fontstyle="italic""2"/mml:mn""mml:mo stretchy="false"")"/mml:mo""/mml:math" a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate. Document type: Article

Abstract

The impact of Automated Vehicles (AVs) on urban geography has been widely speculated, though there is little quantitative evidence in the literature to establish the magnitude of such effects. To quantify the impact of the greater precision of automated driving on the spatial efficiency of off-street parking facilities, we develop a mixed integer nonlinear model (solved via a branch-and-cut approach) and present comparisons against industry-standard requirements for human-driving operation. We demonstrate that gains on the order of 40–50% in spatial efficiency (parking spaces per unit area) are in principle achievable while ensuring that each parked vehicle is independently accessible. We further show that the large majority of these efficiency gains can be obtained under current automotive engineering practice in which only the front two wheels pivot. There is a need for standardized methods that take the parking supply of a city as an input and calculate both the aggregate (citywide) efficiency impacts of automated driving and the spatial distribution of the effects. This study is intended as an initial step towards this objective. Document type: Article