Abstract
Driver behaviour and distraction have been identified as the main causes of rear end collisions. However a promptly issued warning can reduce the severity of crashes, if not prevent them completely. This paper proposes a Forward Collision Warning System (FCW) based on information coming from a low cost forward monocular camera for low end electric vehicles. The system resorts to a Convolutional Neural Network (CNN) and does not require the reconstruction of a complete 3D model of the surrounding environment. Moreover a closed-loop simulation platform is proposed, which enables the fast development and testing of the FCW and other Advanced Driver Assistance Systems (ADAS). The system is then deployed on embedded hardware and experimentally validated on a test track.
Highlights
The rapid population and economic growth of recent years has led to an increasing number of circulating vehicles, inducing traffic congestion, road accidents and pollution
The need to improve driver and pedestrian safety led to the development of on-board active safety systems, which extend the functionalities of the traditional passive systems, such as seat belts and airbags
Time-To-Collision, T, threshold is shown as a constant horizontal line. (b) Time-history of the vehicle speed. (c) Timehistory of the Forward Collision Warning activation
Summary
The rapid population and economic growth of recent years has led to an increasing number of circulating vehicles, inducing traffic congestion, road accidents and pollution. Active systems are developed with the aim to predict the occurrence of an accident, while passive systems are engaged only to soften the consequences In this scenario, Advanced Driving Assistance Systems (ADASs) are recognized as the key enabling technology for the active reduction of the main road transport issues in the very near future [4,5,6,7]. In this perspective, safe and green transport will be possible by embedding the ADAS on full-electric vehicles that, thanks to their simplified high-efficiency powertrains and zero direct emission peculiar features, can greatly improve urban air quality by reduction of COx , NOx and Cx Hy emissions
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