Lytx, Inc.

Jesse Daniels

Vincent Nguyen

Stephen Krotosky

Jeremiah Todd Wittevrongel

Mihir Sathe

The system includes a processor and a memory. The processor is configured to receive data from a camera, determine a plurality of heat maps using a multitask model, and provide the plurality of heat maps. The memory is coupled to the processor and configured to provide the processor with instructions.

Methodology of building a multi task model into a single model

A system for context based alerts includes an interface and a processor. The interface is configured to receive driver context data and surrounding context data. the processor is configured to determine a current state using alert context patterns and the driver context data and the surrounding context data, where the alert context patterns are determined based at least in part on correlation between events in historic event data and context data, and where the context data comprises historic driver context data and historic surrounding context data; and determine whether to alert a driver based at least in part on the current state.

Context driven state machine system for alert generation

A system for training a model comprises an interface and a processor. The interface is configured to receive a general training set and a set of specific data sets. The processor is configured to train a back end model using the general training set; combine the back end model with a first front end model to create a general combined model; train the general combined model with the set of specific data sets; freeze back end weights of the back end model in the general combined model and combine the back end model with a first specific front end model to create a first specific model; train the first specific model with a first specific data set of the set of specific data sets; and provide the first specific model.

Adaptive model for vehicle processing of images

A system for collision detection includes an interface and a processor. The interface is configured to receive sensor data from a plurality of sensors. The processor is configured to preprocess the sensor data using a set of preprocessing steps to create preprocessed sensor data, wherein the set of preprocessing steps comprises filtering, normalization, and alignment; process the preprocessed sensor data using a compound model to create a collision score; and provide the collision score.

Sensor fusion for collision detection

A system for updating training data includes an interface and a processor. The interface is configured to receive a set of vehicle data. The set of vehicle data includes images and assigned labels associated with the images. The processor is configured to determine a set of training data and a set of test data from the set of vehicle data; train a model with the set of training data; determine a set of predicted labels for the set of vehicle data using the model; identify a set of potential mislabeled data using the set of predicted labels and the assigned labels; and determine an updated set of training data by relabeling the set of potential mislabeled data and replacing the set of potential mislabeled data with a relabeled set of data.

Automated training data quality process

A system for identifying data includes an interface and a processor. The interface is configured to receive training data samples associated with interesting events of an interesting event type. The processor is configured to train a model to recognize the interesting events; determine embedded vectors associated with the interesting events; and determine a representative embedded vector associated with the interesting event type.

Signature use for efficient data transfer

A system for characterizing driver behavior includes an interface and a processor. The interface is configured to receive a set of vehicle data, wherein the set of vehicle data comprises embedded image vectors that characterize vehicle data over a short time scale. The processor is configured to determine, using a long time scale model, a long time scale label based at least in part on the embedded image vectors.

Trip based characterization using micro prediction determinations

A system for video analysis includes an interface and a processor. The interface is configured to receive a trigger indication. The processor is configured to determine a time sequence set of video frames associated with the trigger indication; determine a decision based at least in part on an analysis of the time sequence set of video frames; and indicate the decision.

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San Diego, CA, United States of America

Jesse Daniels