Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: An adversarial reinforcement learning system is used to simulate a security checkpoint. The system includes a simulation engine configured to simulate a security checkpoint and various threat objects and threat-mitigation objects therein. The system further includes an attack model configured to control threat objects in the simulation and a defense model configured to control threat-mitigation objects in the simulation. A first portion of the simulation is executed by the simulation engine in order to generate an outcome of the first portion of the simulation. The defense model then generates a threat-mitigation input to control threat-mitigation objects in a subsequent portion of the simulation, and the attack model then generates a threat input to control threat objects in the subsequent portion of the simulation, wherein the inputs are based in part on the outcome of the first portion of the simulation.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Systems and methods are provided for retraining a machine learning model to learn a retrained machine learning model. After a computing system receives an execution log of a prediction service and an observed user action, it may create retraining data. From the retraining data, the computing system may learn a retrained machine learning model. The retrained machine learning model can be used to compute future predictions of user actions if it has an improved performance over the original machine learning model.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: Disclosed herein are embodiments of a clamp assembly for attaching a tonneau cover to a vehicle. The clamp assembly can include a bow hanger comprising a main body extending between first and second ends and a protrusion extending away from the main body and along a portion of a length thereof. The clamp assembly can have a bow slide comprising a head configured to surround and move along the bow hanger. The clamp assembly can have a slot configured to receive the protrusion of the bow hanger, wherein engagement between the protrusion and the slot of the bow slide prevents the bow slide from rotating around the bow hanger. In some embodiments, the clamp assembly includes a handle coupled to the bow slide and a cam follower. The cam follower can engage rails of the handle which can be disposed either internally or externally of the handle.