Abstract: Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

Abstract: A method for generating a motion plan for an autonomous agent includes obtaining scene data for an agent's environment, including map data and historical state data for one or more objects. A transformer-based encoder generates a set of scene embedding tokens from the scene data to represent a fixed environmental context. A transformer-based decoder autoregressively generates a sequence of action tokens representing a future trajectory. The generation of each subsequent action token is based on the scene embedding tokens and previously generated tokens. Each action token is selected from a discrete action space of unique Verlet actions, which represent accelerations. A future trajectory is determined from the sequence of action tokens and provided to a motion planning module of the autonomous agent.

Abstract: A method includes receiving initial conditions for a traffic scenario including a plurality of interacting agents, the initial conditions defining states of the plurality of agents, identifying a causal structure among the plurality of agents based on the states of the plurality of agents, and ranking the plurality of agents based on the identified causal structure to determine a subset of key agents being most influential with respect to a controllability objective. For each agent of the plurality of agents, the method includes generating a future trajectory using a reverse sampling process of a diffusion model, and guiding the reverse sampling process by selectively applying a gradient of the controllability objective only to the determined subset of key agents.

Abstract: Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

Abstract: Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

Abstract: Disclosed herein is a method and system for detecting, monitoring and/or controlling one or more of mobile services for a mobile communication device (also referred to herein as a Controllable Mobile Device or CMD), and in particular, when the device is being used and the vehicle, operated by the user of the device, is moving. In addition, one aspect of the invention generally relates to a method and system for modifying a user's driving behaviors, in particular to a system and method for modifying a user's unsafe driving behaviors, e.g., using one or more services of a controllable mobile device while driving.

Abstract: Provided are methods for generating future maneuver parameters in a planner, which include receiving at least one first set of parameters associated with one or more previous maneuvers of a vehicle and at least one second set of parameters associated with a maneuver goal of the vehicle, generating, using the first and second sets of parameters, a future maneuver parameter corresponding to a future maneuver of the vehicle, training at least one data model by comparing the generated future maneuver parameter to one or more reference maneuver parameters, generating, based on training, a corrected future maneuver parameter. The corrected future maneuver parameter includes a future maneuver of the vehicle and a correction to the future maneuver of the vehicle. The first set of parameters includes the generated corrected future maneuver parameter, which is used to correct at least one first parameter. The training is executed using the corrected first parameter.

Abstract: A scene simulation system can use scene data of a scene of a vehicle to generate one or more simulated states and one or more simulated trajectories associated with the one or more simulated states. The system can evaluate the simulated trajectories and select an action for the vehicle based on the evaluation of the simulated trajectories.

Abstract: Provided are methods for predicting agent trajectories, which can include generating a graph corresponding to a map of a scene by encoding map features and agent features as node encodings of the graph and determining a policy for application to outgoing edges of the nodes of the graph. Some methods described also include sampling paths for a target vehicle in the scene according to the policy and predicting a set of trajectories based on the sampled paths traversed by the policy and a sampled latent variable. Systems and computer program products are also provided.

Abstract: Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

Abstract: Provided are methods for predicting agent trajectories, which can include generating a graph corresponding to a map of a scene by encoding map features and agent features as node encodings of the graph and determining a policy for application to outgoing edges of the nodes of the graph. Some methods described also include sampling paths for a target vehicle in the scene according to the policy and predicting a set of trajectories based on the sampled paths traversed by the policy and a sampled latent variable. Systems and computer program products are also provided.

Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, sensor data associated with a surface along a path to be traveled by a vehicle; using a surface classifier to determine a classification of the surface based on the sensor data; determining, based on the classification of the surface, drivability properties of the surface; planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface; and controlling the vehicle based on the planned behavior.

Abstract: Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

Abstract: Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.

Abstract: Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

Abstract: Among other things, techniques are described for driver data guided spatial planning. A spatial structure is generated comprising a plurality of nodes connected by edges. At least some of the nodes and edges represent a path to navigate a vehicle from a first point to a second point. Edges of the spatial structure are labeled as useful based on a distance metric. The spatial structure is pruned by removing one or more edges from the spatial structure according to a respective label of the edges, wherein an extent of the removal is based on a predetermined graph size, a predetermined performance, or any combinations thereof to obtain a pruned graph. A path from the first point to the second point on the pruned graph is identified and the vehicle is navigated in accordance with the path from the first point to the second point on the pruned graph.

Abstract: Enclosed are embodiments for sampling driving scenarios for training machine learning models. In an embodiment, a method comprises: assigning, using at least one processor, a set of initial physical states to a set of objects in a map for a set of simulated driving scenarios, wherein the set of initial physical states are assigned according to one or more outputs of a random number generator; generating, using the at least one processor, the set of simulated driving scenarios in the map using the initial physical states of the objects in the set of objects; selecting, using the at least one processor, samples of the simulated driving scenarios; training, using the at least one processor, a machine learning model using the selected samples; and operating, using a control circuit, a vehicle in an environment using the trained machine learning model.

Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Provided are methods for generating future maneuver parameters in a planner, which include receiving at least one first set of parameters associated with one or more previous maneuvers of a vehicle and at least one second set of parameters associated with a maneuver goal of the vehicle, generating, using the first and second sets of parameters, a future maneuver parameter corresponding to a future maneuver of the vehicle, training at least one data model by comparing the generated future maneuver parameter to one or more reference maneuver parameters, generating, based on training, a corrected future maneuver parameter. The corrected future maneuver parameter includes a future maneuver of the vehicle and a correction to the future maneuver of the vehicle. The first set of parameters includes the generated corrected future maneuver parameter, which is used to correct at least one first parameter. The training is executed using the corrected first parameter.

Abstract: Among other things, techniques are described for screening and monitoring the health of a vehicle user including receiving sensor data produced by a sensor at the vehicle, processing the sensor data to determine at least one health condition of the user of the vehicle, and in response to determining the at least one health condition, executing a vehicle function selected from a plurality of vehicle functions based on the at least one health condition.