How to Cite
Abstract
[1]Autonomous driving is entering our lives in the form of commercial vehicles and private cars at an increasing pace. Many advanced features such as automatic parking, adaptive cruise control, and lane-keeping assistance are already available in most modern cars. Also, the first vehicles with conditional (ef.) or supervised automation are already commercially available in limited areas, and fully autonomous vehicles are being tested in many places around the world. At the same time, the possibilities of artificial intelligence and machine learning are constantly growing, partly due to many resources that are made public. In addition, machine learning algorithms that can handle image, audio, and video inputs are becoming more accurate and available. Consequently, it seems like a natural development to use such perception methods in autonomous vehicles to improve and develop the interaction between vehicles, drivers, and other road users [2].[3] One aspect of closely related research is understanding and predicting the intentions of vulnerable road users (VRU) in traffic (Stol, El Aad, & De Winter, 2019a). This is an essential part of the basic traffic tasks scanning, hazard detection, decision-making and driving behavior planning, as summarized by Dewitte et al. (2019). Consequently, there is a distinction between intentions, which need to be considered before driving actions, and actions that are currently observable. Both in detail analysis and in learning the surrounding traffic, the intention of the VRUs is therefore an important piece of information.
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