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Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (e.g. rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference images stored in the cloud.

Traditional Heat Ventilation and Air Conditioning (HVAC) control systems are reactive by design and largely dependent on the on-board sensory data available on a Controller Area Network (CAN) bus. The increasingly common Internet connectivity offered in today's vehicles, through infotainment and telematic systems, makes data available that may be used to improve current HVAC systems. This includes real-time outside relative humidity, ambient temperature, precipitation (i.e., rain, snow, etc.), and weather forecasts. This data, combined with position and route information of the vehicle, may be used to provide a more comfortable experience to vehicle occupants in addition to improving driver visibility through more intelligent humidity, and defrost control. While the possibility of improving HVAC control utilizing internet connectivity seems obvious, it is still currently unclear as to what extent.