US9254824 illustrates an adaptive anti-collision system for providing timely alert information by analyzing the driving pattern of the driver using a neural network. A neural network utilizes massive connected artificial neurons to mimic the capability of a biological neural network so as to acquire information from external environment. In essence, a neural network is an attempt to simulate the human brain.
The adaptive anti-collision system determines the driving pattern corresponding to a vehicle speed, a safe distance and a braking distance based on the vehicle speed or acceleration, road condition, and drivers’ driving behavior using the neural network. Then, the adaptive anti-collision system adjusts control parameters of the vehicle (e.g., safe distance) control unit dynamically according to the driving pattern to issue an alert or activate a braking action.
US20140108307 illustrates a system for providing personalized and context-based suggestions to a driver using a machine learning. For instance, the system obtains contextual information indicating that a contact of the driver is only a few minutes in driving time from the driver’s route. Further, based on social communications and social media information stored in the driver profile, the system also determines that the contact is a friend of the driver. Based on that determination, the system informs the driver, “Your friend Peter is at a few minutes away from your route; would you like meet?”
US20150302718 illustrates a system for correlating physiological signals associated with a driver with vehicle-related events using a machine learning. Physiological signals include sensed and monitored data regarding the heart-rate, oxygen use, eye motion, galvanic skin response, blood flow, pupil dilation, and facial expression. Vehicle-related events include traffic, weather, visibility, road conditions, accidents, traffic alerts, distance-from-other vehicles. Vehicle-related events can be determined and communicated through external sources (e.g., cloud-data, inter-vehicle communication) as well as the vehicle’s controller-area network.
Based on the vehicle event data and physiological data, a driver state is then determined by correlating the vehicle event data with the physiological data associated with the driver using the machine learning. The driver state includes a level of driver stress, a level of driver drowsiness, a level of fear of the driver, a state correlated to the event of overtaking another driver, and a state correlating to the event of being overtaken by another driver. The system provides a suggested action based on the state of the driver. For example, the system determines that the driver becomes angry or nervous when in heavy traffic. The system then suggests to the driver that the channel on the audio system be changed to provide relatively soothing music.
US8190319 illustrates an adaptive real-time driver advisory control system for a hybrid electric vehicle to achieve fuel economy improvement using a fuzzy logic. A fuzzy logic-based adaptive algorithm with a learning capability can estimate a driver’s long term driving preferences. The advisory control system uses a set of rules with fuzzy predicates and an approximate reasoning method to summarize a strategy that accounts for instantaneous fuel consumption, vehicle speed, vehicle acceleration, and the driver’s torque request, in order to determine the upper bound of the torque request that accounts for maximum fuel efficiency and drivability. The advisory control system then provides feedback to the driver such that the fuel economy of the vehicle can be improved in a real world driving environment.
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