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Autonomous driving technology has advanced significantly in recent years, thanks to the innovations in vehicle design and artificial intelligence. This technology provides a more convenient and comfortable driving experience for both drivers and passengers. However, current autonomous driving systems still face some difficulties, such as the need for human intervention in critical situations. Switching control from the system to the driver without assessing their alertness or health status could pose a risk. Therefore, autonomous driving systems should be able to monitor and evaluate the driver's condition before handing over the control. This paper thus proposes an in-cabin detection system that monitors driver status using a camera. The system consists of software modules that use deep learning techniques to analyze the input from an IR camera.

Forward safety system (FSS) monitors the roadway ahead and warns the driver when a potential risk exists. The vision-based FSS can provide better protection capability than those based on radars because it owns rich environmental information, such as the shape and color of lane lines and objects. However, the detection of vision-based FSS is generally restricted to a small area in front of the vehicle due to the hardware limitation of the adopted camera. This paper presents a novel FSS which utilizes cameras with different focal lengths to extend the detection range and thus can provide early lane departure and forward collision warning. The proposed FSS extracts lane lines by the intensity and structural properties, and detects potential lane departure and forward collision by analyzing the moving trajectory and by detecting the obstacle ahead, respectively.