Abstract: A group of state-action history entries may be determined from state-action history entries stored in a database. A state-action history entry may represent an experienced operational scenario. A state-action history entry may be associated with a feature. The group of state-action history entries may be determined based on a similarity of the feature. A parameter may be generated based on the group of state-action history entries. The parameter may represent a probability associated with experienced operational scenarios that are similar to one another. A model may be generated based on the parameter. The model may be configured for use in an operational scenario that is similar to the experienced operational scenarios when traversing a portion of the vehicle transportation network.

Abstract: Aspects of the disclosure relate to a dynamic distance estimation output platform that utilizes improved computer vision and perspective transformation techniques to determine vehicle proximities from video footage. A computing platform may receive, from a visible light camera located in a first vehicle, a video output showing a second vehicle that is in front of the first vehicle. The computing platform may determine a longitudinal distance between the first vehicle and the second vehicle by determining an orthogonal distance between a center-of-projection corresponding to the visible light camera, and an intersection of a backside plane of the second vehicle and ground below the second vehicle. The computing platform may send, to an autonomous vehicle control system, a distance estimation output corresponding to the longitudinal distance, which may cause the autonomous vehicle control system to perform vehicle control actions.

Abstract: Aspects of the disclosure relate to a dynamic distance estimation output platform that utilizes improved computer vision and perspective transformation techniques to determine vehicle proximities from video footage. A computing platform may receive, from a visible light camera located in a first vehicle, a video output showing a second vehicle that is in front of the first vehicle. The computing platform may determine a longitudinal distance between the first vehicle and the second vehicle by determining an orthogonal distance between a center-of-projection corresponding to the visible light camera, and an intersection of a backside plane of the second vehicle and ground below the second vehicle. The computing platform may send, to an autonomous vehicle control system, a distance estimation output corresponding to the longitudinal distance, which may cause the autonomous vehicle control system to perform vehicle control actions.

Abstract: According to one aspect, a system for reinforcement learning with iterative reasoning may include a memory for storing computer readable code and a processor operatively coupled to the memory, the processor configured to receive a level-0 policy and a desired reasoning level n. The processor may repeat for k=1 . . . n times, the following: populate a training environment with a level-(k?1) first agent, populate the training environment with a level-(k?1) second agent, and train a level-k agent based on the level-(k?1) first agent and the level-(k?1) second agent to derive a level-k policy.

Abstract: Aspects of the disclosure relate to a dynamic distance estimation output platform that utilizes improved computer vision and perspective transformation techniques to determine vehicle proximities from video footage. A computing platform may receive, from a visible light camera located in a first vehicle, a video output showing a second vehicle that is in front of the first vehicle. The computing platform may determine a longitudinal distance between the first vehicle and the second vehicle by determining an orthogonal distance between a center-of-projection corresponding to the visible light camera, and an intersection of a backside plane of the second vehicle and ground below the second vehicle. The computing platform may send, to an autonomous vehicle control system, a distance estimation output corresponding to the longitudinal distance, which may cause the autonomous vehicle control system to perform vehicle control actions.

Abstract: Systems and methods are presented that enable logical reasoning even in the Presence of noisy (inconsistent) data. The knowledge base is processed in order to make it consistent and is also compiled. This processing includes checking and correcting spelling, removing stopwords, performing, grouping words of similar and related meaning, and compacting the knowledge base. A robot can use the processed knowledge base to perform many different types of tasks, such as answering a query, determining a course of action that is designed to achieve a particular goal, and determining its own location.