Imitation Learning for Vision-based Lane Keeping Assistance

Innocenti, Christopher; Lindén, Henrik; Panahandeh, Ghazaleh; Svensson, Lennart; Mohammadiha, Nasser

doi:10.48550/arxiv.1709.03853

Cited by 1 publication

(3 citation statements)

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“…Deep learning [14,19,25] can be used to analyze and process input data received from the automobile sensors such as cameras, motion sensors, laser light, GPS, Odometry, LiDAR, and radar sensors and control the vehicle in response to information from various sensors [4,10,12,13,28]. Also, we can utilize computer vision for eye-gaze tracking [10,13,25,28], monitoring threshold blinking [22] and head movement [24] which in turn such in-car sensing technologies would enable us to warn the driver of drowsiness or distraction in real-time.…”

Section: Introductionmentioning

confidence: 99%

“…Also author in [1] fused radar and camera data to improve the perception of the vehicle's surrounding, including road features and obstacles and pedestrians. As in [1,3,5,7,14,19] authors presented an assist system in which they utilize machine vision techniques to recognize road lanes and signs. These progressive image processing methods infer lane data from forward-facing cameras mounted at the front of the vehicle [1,3,7].…”

Section: Introductionmentioning

confidence: 99%

“…These progressive image processing methods infer lane data from forward-facing cameras mounted at the front of the vehicle [1,3,7]. Some of the advanced lane finding algorithms have been developed using deep learning and neural network approaches [5,14,19]. Some other procedures used for monitoring the consciousness and emotional status of the driver are momentous for the safety and comfort of driving.…”

Section: Introductionmentioning

confidence: 99%

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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 encoder-decoder networks 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.

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