Abstract: A method and system for detecting malicious threat activity or event sequences is disclosed. In an embodiment, the method may include receiving security data from a plurality of data sources and normalizing the security data. The method may include generating one or more statistical profiles for one or more entities based on the normalized data. The method may include generating one or more detectors based on one or more subsequences organized in a plurality of threat chains. The method may include monitoring, via the one or more detectors, telemetric data in real time for the one or more subsequences. The method may include aggregating each detected one or more subsequences. The method may include generating a score based on a correlation of aggregated detected subsequences to the one or more statistical profiles. The method may include, if the score of exceeds a threshold, generating a high severity alert.

Abstract: A method of controlling availability of autonomy functions of a vehicle includes determining one or more uncertainty levels, each corresponding to a driving data stream including driving data, wherein the uncertainty levels correspond to a probability of a collision or driving off the roadway. The method further includes generating a total uncertainty level based on the one or more uncertainty levels and disabling a first autonomy function based on availability criteria of the first autonomy function. The availability criteria define a first threshold of the total uncertainty level. The method further includes providing a second autonomy function based on the second autonomy function's availability criteria that include a total uncertainty threshold higher than the first threshold.

Abstract: A method for identifying high-risk driving situations in driving data may include receiving driving data, identifying trigger events in the driving data, and using the trigger events in combination with first portions of the driving data corresponding to periods of time preceding the trigger events, training a machine learning model to identify features, in the driving data, associated with the trigger events. The method may further include using second portions of the driving data where no trigger events are identified, further training the machine learning model to ignore features common to both the first portions and the second portions. The method further includes detecting a high risk driving situation in real time driving data by using the machine learning model to identify the features associated with the trigger events, and performing a defensive driving maneuver in response to the detection of the high risk driving situation.

Abstract: A method and system for detecting malicious threat activity or event sequences is disclosed. In an embodiment, the method may include receiving security data from a plurality of data sources and normalizing the security data. The method may include generating one or more statistical profiles for one or more entities based on the normalized data. The method may include generating one or more detectors based on one or more subsequences organized in a plurality of threat chains. The method may include monitoring, via the one or more detectors, telemetric data in real time for the one or more subsequences. The method may include aggregating each detected one or more subsequences. The method may include generating a score based on a correlation of aggregated detected subsequences to the one or more statistical profiles. The method may include, if the score of exceeds a threshold, generating a high severity alert.

Abstract: A method of controlling availability of autonomy functions of a vehicle includes determining one or more uncertainty levels, each corresponding to a driving data stream including driving data, wherein the uncertainty levels correspond to a probability of a collision or driving off the roadway. The method further includes generating a total uncertainty level based on the one or more uncertainty levels and disabling a first autonomy function based on availability criteria of the first autonomy function. The availability criteria define a first threshold of the total uncertainty level. The method further includes providing a second autonomy function based on the second autonomy function's availability criteria that include a total uncertainty threshold higher than the first threshold.

Abstract: A method for identifying high-risk driving situations in driving data may include receiving driving data, identifying trigger events in the driving data, and using the trigger events in combination with first portions of the driving data corresponding to periods of time preceding the trigger events, training a machine learning model to identify features, in the driving data, associated with the trigger events. The method may further include using second portions of the driving data where no trigger events are identified, further training the machine learning model to ignore features common to both the first portions and the second portions. The method further includes detecting a high risk driving situation in real time driving data by using the machine learning model to identify the features associated with the trigger events, and performing a defensive driving maneuver in response to the detection of the high risk driving situation.

Abstract: Systems and methods for controlling a vehicle with respect to an intersection are disclosed. In one embodiment, a method of controlling a vehicle with respect to an intersection, includes determining a distance of the vehicle with respect to the intersection, wherein the intersection includes a traffic light, determining a velocity of the vehicle, and receiving traffic light state information regarding the traffic light. The method also includes calculating, based on the distance of the vehicle to the intersection, the velocity of the vehicle, and the traffic light state information, a zone of interest with respect to the traffic light. The method further includes manipulating the velocity of the vehicle to modify a size of the zone of interest when a current trajectory of the vehicle will cause the vehicle to enter the zone of interest.