Abstract
Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending more effort trying to understand the interaction between vehicles with various levels of automation in large-scale traffic scenarios. Next-generation vehicles are expected to have the capacity of evaluating driver conditions, vehicle capabilities, surrounding traffic contexts, and take advantage of such knowledge to ensure safe and efficient driving. Three general research questions are raised to achieve this goal, which are (i) how can we acquire sufficient data, (ii) how to evaluate and understand driving behavior, and (iii) how to deliver information effectively to drivers. In this article, we present a review of previous studies from the UTDrive project attempts to answer above questions.
Highlights
Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years
The results demonstrate the adaptive capabilities of deep learning, reinforce the claim that with the increasing availability of pre-trained high-performance deep learning models, new problems can be addressed without collecting extensive dedicated datasets for them
Our experiments demonstrate the adaptive capabilities of deep learning
Summary
Driving is a complex multi-task process that involves extensive human-machine interaction such as monitoring the environment and surrounding vehicle conditions, predicting other driver’s movement and potential risks, determining the best action of their vehicle, and executing the maneuver by controlling the gas/brake pedals and steering wheels to ensure safety This results in an increased high standard for drivers to operate a vehicle safely on the road. By using the vehicle’s onboard sensors or combine information through vehicle communication, next-generation intelligent vehicles should have the ability to evaluate and understand the driver’s status, performance, and driving behavior As a result, such systems could warn of protentional risks, provide guidance when necessary (e.g., lane level guidance), and make essential adjustments or actions when critical.
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