One of the major hurdles in high level autonomous driving is the integration of software that has the ability to understand its surrounding situations. Situation awareness is to perceive information, take actions based on that perception and to predict the future events based on the taken actions. Mica R. Endsley introduced the term “Situation Awareness” where the concept was applied only for humans. Expert Systems, on the other hand is a rule based system that is used to make critical and fast decisions. In this paper a concept on the comprehension (understanding) level of situation awareness using an expert system is presented for high level autonomous vehicles. This concept will be realized on the “CE-Box” component of the hardware demonstrator “BlackPearl” from the department of Computer Engineering in TU Chemnitz. Perception is done with the help of sensing data from the sensors from the ECU’s of “BlackPearl”. This data is provided to the comprehension level to make an assessment and take a decision. The goal of this paper is to provide a conceptual method to extract these decisions based on an expert system.

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This paper introduces a concept for the perception level of situation awareness through image processing applications. The perception of the information from the environment is lacking to achieve highly autonomous vehicles. This approach mainly focuses on collecting information from the environment using camera sensors. The frames from the camera are processed using multiple image processing algorithms, outputs converted into CAN message, and sent to the expert system. CE-Box custom hardware that consists of a Raspberry Pi 3b model counted with PiCAN 2 used to implement and evaluate this approach. The goal of this paper is to provide a conceptual method to make decisions based on the extracted information from the environment using image processing algorithms.

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Automotive Industry is having a rapid progress towards highest level of autonomy. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. In order to automate this process, first, understanding and automating the situation identification is necessary. Systems will also have to have embedded intelligence of awareness in order to reach to these situations. Situation Awareness is a term that consists of extracting information from the environment, providing an understanding towards the extracted features and taking actions in order to make awareness. This journal focuses on the different levels of situation awareness, provides concepts in order to automate the process so that it can play a vital role towards highly autonomous vehciles.