Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles.
autonomous vehicle
embedded devices
input task modeling
internet of things
mixed-criticality task scheduling
real-time systems
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
02 Nov 2019
02 Nov 2019
Historique:
received:
28
08
2019
revised:
22
10
2019
accepted:
25
10
2019
entrez:
6
11
2019
pubmed:
7
11
2019
medline:
1
4
2020
Statut:
epublish
Résumé
Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO 2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life.
Identifiants
pubmed: 31684010
pii: s19214761
doi: 10.3390/s19214761
pmc: PMC6864586
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Commentaires et corrections
Type : ErratumIn
Références
Accid Anal Prev. 2011 Jul;43(4):1285-90
pubmed: 21545856
PLoS One. 2016 Dec 20;11(12):e0168054
pubmed: 27997566
PLoS One. 2017 Sep 20;12(9):e0184952
pubmed: 28931022