An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles

12/10/2018
by   Hadi Abdi Khojasteh, et al.
0

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encode-decoder network and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.

READ FULL TEXT

page 2

page 6

page 8

page 10

03/19/2018

Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation

When a semi-autonomous car crashes and harms someone, how are blame and ...
01/02/2021

Smart Car Features using Embedded Systems and IoT

There has been a tremendous rise in technological advances in the field ...
05/25/2022

Certify the Uncertified: Towards Assessment of Virtualization for Mixed-criticality in the Automotive Domain

Nowadays, a feature-rich automotive vehicle offers several technologies ...
11/22/2018

Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs

Recognizing driver behaviors is becoming vital for in-vehicle systems th...
02/05/2021

Maintaining driver attentiveness in shared-control autonomous driving

We present a work-in-progress approach to improving driver attentiveness...
02/25/2021

Machine Biometrics – Towards Identifying Machines in a Smart City Environment

This paper deals with the identification of machines in a smart city env...
04/23/2021

Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data

With increasing automation in passenger vehicles, the study of safe and ...