Abstract: Provided are methods and systems for lane change and intent determination. A method for operating an autonomous vehicle is provided. The method includes obtaining, first scene data associated with a scene of an autonomous vehicle. The method includes generating a plurality of trajectories for the autonomous vehicle. The method includes selecting a trajectory from the plurality of trajectories. The method includes determining a vehicle action intent of the selected trajectory. The method includes combining the vehicle action intent of the selected trajectory with a set of vehicle action intents to form a plurality of vehicle action intents. The set of vehicle action intents correspond to a set of trajectories generated prior to the selected trajectory from second scene data. The method includes selecting a vehicle action for the autonomous vehicle and causing the autonomous vehicle to initiate performance of the vehicle action based on the selecting the action.

Abstract: A backlight module includes a light source, a first prism sheet disposed on the light source, and a light type adjustment sheet disposed on a side of the first prism sheet away from the light source and including a base and multiple light type adjustment structures. The multiple light type adjustment structures are disposed on the first surface of the base. Each light type adjustment structure has a first structure surface and a second structure surface connected to each other. The first structure surface of each light type adjustment structure and the first surface of the base form a first base angle therebetween, and the second structure surface of each light type adjustment structure and the first surface of the base form a second base angle therebetween. The angle of the first base angle is different from the angle of the second base angle.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

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: A quantum dot composition includes a matrix resin, a quantum dot phosphor, and a polysilane polymer. The matrix resin includes epoxy-fluorene copolymer acrylic resin represented by Formula 1: Formula 1. In Formula 1, R1 and R4 each is independently hydrogen or a C1-C12 long alkyl carbon chain. R2 and R3 each is independently a is an integer from 1 to 10, and b and c each is independently an integer from 0 to 10. X is 0.1 to 0.9. A color conversion film including the quantum dot composition and a backlight module using the color conversion film 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: Using a planning circuit of a vehicle, a map is accessed that includes information identifying at least one lane on which vehicles can travel. Using the planning circuit and from the map, a graph representing a driving environment of the vehicle is generated. The graph includes multiple trajectories. At least one trajectory includes a lane change. Each trajectory is a path for the vehicle to move from a first spatiotemporal location to a second spatiotemporal location. The trajectory includes at least one lane alone which the vehicle can move. Using the planning circuit, a trajectory of the multiple trajectories for the vehicle to travel is selected based on an initial vehicle trajectory of the vehicle. The selected trajectory includes a stem. The stem is a portion of the selected trajectory to which the vehicle is configured to adhere. Using the control circuit, the vehicle is moved along the selected trajectory.

Abstract: Among other things, techniques are described for collision free path generation by connecting C-slices through cell decomposition. An environment is sampled at discrete headings of a vehicle to generate a configuration space (C-space) with one or more C-slices. A first C-slice is decomposed into one or more cells that represent free space. A C-slice adjacency list is generated for the first C-slice. A super adjacency list is derived that connects vertices of interest across the one or more C-slices to form a super adjacency graph. In embodiments, Dubins path is used for connecting the vertices of interest both within and across C-slices to ensure the kinematic feasibility of all the searched paths. An optimal path is navigated, wherein the optimal path is a shortest path from a starting pose to a goal pose on the super adjacency graph.

Abstract: Using a planning circuit of a vehicle, a map is accessed that includes information identifying at least one lane on which vehicles can travel. Using the planning circuit and from the map, a graph representing a driving environment of the vehicle is generated. The graph includes multiple trajectories. At least one trajectory includes a lane change. Each trajectory is a path for the vehicle to move from a first spatiotemporal location to a second spatiotemporal location. The trajectory includes at least one lane alone which the vehicle can move. Using the planning circuit, a trajectory of the multiple trajectories for the vehicle to travel is selected based on an initial vehicle trajectory of the vehicle. The selected trajectory includes a stem. The stem is a portion of the selected trajectory to which the vehicle is configured to adhere. Using the control circuit, the vehicle is moved along the selected trajectory.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

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 determination is made that intervention in an operation of one or more autonomous driving capabilities of a vehicle is appropriate. Based on the determination, a person is enabled to provide information for an intervention. The intervention is caused in the operation of the one or more autonomous driving capabilities of the vehicle.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: An autonomous vehicle receives sensor data from one or more sensors of the autonomous vehicle, and generates a request for remote control of the autonomous vehicle by a computer system remote from the autonomous vehicle. Generating the request includes determining a quality metric associated with a network connection between the autonomous vehicle and the computer system, and upon determining that the quality metric is greater than a threshold quality level, transmitting, from the autonomous vehicle to the computer system the request for remote control and a first data item representing the sensor data. The first data item meets one or more conditions associated with the threshold quality level.

Abstract: Among other things, a determination is made that intervention in an operation of one or more autonomous driving capabilities of a vehicle is appropriate. Based on the determination, a person is enabled to provide information for an intervention. The intervention is caused in the operation of the one or more autonomous driving capabilities of the vehicle.

Abstract: A quantum dot composition includes a matrix resin, a quantum dot phosphor, and a polysilane polymer. The matrix resin includes epoxy-fluorene copolymer acrylic resin represented by Formula 1: Formula 1. In Formula 1, R1 and R4 each is independently hydrogen or a C1-C12 long alkyl carbon chain. R2 and R3 each is independently a is an integer from 1 to 10, and b and c each is independently an integer from 0 to 10. X is 0.1 to 0.9. A color conversion film including the quantum dot composition and a backlight module using the color conversion film are also provided.

Abstract: An autonomous vehicle receives sensor data from one or more sensors of the autonomous vehicle, and generates a request for remote control of the autonomous vehicle by a computer system remote from the autonomous vehicle. Generating the request includes determining a quality metric associated with a network connection between the autonomous vehicle and the computer system, and upon determining that the quality metric is greater than a threshold quality level, transmitting, from the autonomous vehicle to the computer system the request for remote control and a first data item representing the sensor data. The first data item meets one or more conditions associated with the threshold quality level.

Abstract: Among other things, a determination is made that intervention in an operation of one or more autonomous driving capabilities of a vehicle is appropriate. Based on the determination, a person is enabled to provide information for an intervention. The intervention is caused in the operation of the one or more autonomous driving capabilities of the vehicle.