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Investments in autonomous driving technology have already exceeded $200 billion, yet despite this staggering figure, driving in the rain is still an unmet need. Current self-driving vehicles are unable to drive during unexpected showers and will simply park on the side of the road. Navigating in the rain without proper cleaning technology is very challenging, water droplets will accumulate on lenses and cause distorted visibility. The solution presented in this report, named Dryve, consists of a redesigned camera case that uses a combination of high-speed rotation, hydrophobic coating, and air blowers to keep the camera lens free of droplets. By preventing rain droplets from accumulating on the camera lens, Dryve can strongly improve visibility and sensing accuracy. Dryve strives to enable safe and autonomous driving during rainy conditions, hence further encouraging the mass adoption of self-driving technology.

Investments in autonomous driving technology have already exceeded $200 billion, yet despite this staggering figure, driving in the rain is still an unmet need. Current self-driving vehicles are unable to drive during unexpected showers and will simply park on the side of the road. Navigating in the rain without proper cleaning technology is very challenging, water droplets will accumulate on lenses and cause distorted visibility. The solution presented in this report, named Dryve, consists of a redesigned camera case that uses a combination of high-speed rotation, hydrophobic coating, and an air blower to keep the camera lens free of droplets. Dryve also consists of a User Interface by which fleet managers of autonomous vehicles can monitor vehicle status and sensor cleaning. By preventing rain droplets from accumulating on the camera lens, Dryve can strongly improve visibility and sensing accuracy. Dryve strives to enable safe and autonomous driving during rainy conditions, hence further encouraging the mass adoption of self-driving technology.

The amount of data generated, processed, and stored by the modern vehicle is increasing and this is creating the potential to detect abnormal and potentially dangerous situations occurring. The purpose of this thesis is to portray a lack of information in the area of intrusion detection using automotive data and to lay the foundations of research in intrusion detection using unsupervised machine learning. As vehicles continue to become more connected, there is an increased possibility of them being exploitable through a successful cyberattack. An example of a hacked Jeep Cherokee (Miller, Valasek, (2011)) and a remote exploitation strategy using multiple attack vectors (Checkoway et al, (2011)) was the prime exhibition of a situation where the vehicle can be remotely compromised. These examples demonstrate the potential to exploit aspects of the vehicle’s communication and control systems, resulting in expected behavior. This thesis is focused on detecting attacks targeting a vehicle by identifying abnormal vehicle behavior, exhibited through control data. To achieve this, synthetic vehicle data containing detectable abnormalities is generated and used for analysis and detection to help detect cyberattacks. Unsupervised machine learning techniques are used as a way to detect abnormal entries in-vehicle data. the synthetic data is generated based on datasets comparable with those generated during normal vehicle operations, before being used to insert manually insert skewness to generate abnormalities, before using and evaluating various unsupervised learning algorithms.