Abstract: A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.

Abstract: Systems and methods for autonomous lane level navigation are disclosed. In one aspect, a control system for an autonomous vehicle includes a processor and a computer-readable memory configured to cause the processor to receive a partial high-definition (HD) map that defines a plurality of lane segments that together represent one or more lanes of a roadway, the partial HD map including at least a current lane segment. The processor is also configured to generate auxiliary global information for each of the lane segments in the partial HD map. The processor is further configured to generate a subgraph including a plurality of possible routes between the current lane segment and the destination lane segment using the partial HD map and the auxiliary global information, select one of the possible routes for navigation based on the auxiliary global information, and generate lane level navigation information based on the selected route.

Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

Abstract: A shooting method includes: receiving N inputs performed by a user on a preset marquee; and updating a display position of the marquee and sequentially shooting N target images in response to the N inputs. The target image is an image of a target area on a shooting preview interface, and the target area is a whole area of the shooting preview interface or an area selected by the marquee; and N is a positive integer.

Abstract: In some examples, a touch screen includes a first region corresponding to a region of the touch screen without touch electrodes; a second region corresponding to a region of the touch screen with a first conductive material (e.g., solid metal) with a first density in a first conductive layer; and a third region corresponding to a region of the touch screen with a second conductive material (e.g., metal mesh) with a second density, lower than the first density, in the first conductive layer. In some examples, the second region circumscribes the first region, and the third region circumscribes the second region. Some touch electrodes include a portion of the first conductive material in the second region and a portion of the second conductive material in the third region. Such touch electrodes can be routed using the first conductive material in the first conductive layer around the first region.

Abstract: A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed.

Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

Abstract: A system and method for real world autonomous vehicle perception simulation are disclosed. A particular embodiment includes: configuring a sensor noise modeling module to produce simulated sensor errors or noise data with a configured degree, extent, and timing of simulated sensor errors or noise based on a set of modifiable parameters; using the simulated sensor errors or noise data to generate simulated perception data by simulating errors related to constraints of one or more of a plurality of sensors, and by simulating noise in data provided by a sensor processing module corresponding to one or more of the plurality of sensors; and providing the simulated perception data to a motion planning system for the autonomous vehicle.

Abstract: A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.

Abstract: A data-driven prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment includes: generating a first suggested trajectory for an autonomous vehicle; generating predicted resulting trajectories of proximate agents using a prediction module; scoring the first suggested trajectory based on the predicted resulting trajectories of the proximate agents; generating a second suggested trajectory for the autonomous vehicle and generating corresponding predicted resulting trajectories of proximate agents, if the score of the first suggested trajectory is below a minimum acceptable threshold; and outputting a suggested trajectory for the autonomous vehicle wherein the score corresponding to the suggested trajectory is at or above the minimum acceptable threshold.

Abstract: An autonomous vehicle simulation system for analyzing motion planners is disclosed. A particular embodiment includes: receiving map data corresponding to a real world driving environment; obtaining perception data and configuration data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating simulated perception data for each of the plurality of simulated dynamic vehicles based on the map data, the perception data, and the configuration data; receiving vehicle control messages from an autonomous vehicle control system; and simulating the operation and behavior of a real world autonomous vehicle based on the vehicle control messages received from the autonomous vehicle control system.

Abstract: A system and method for adaptive cruise control with proximate vehicle detection are disclosed. The example embodiment can be configured for: receiving input object data from a subsystem of a host vehicle, the input object data including distance data and velocity data relative to detected target vehicles; detecting the presence of any target vehicles within a sensitive zone in front of the host vehicle, to the left of the host vehicle, and to the right of the host vehicle; determining a relative speed and a separation distance between each of the detected target vehicles relative to the host vehicle; and generating a velocity command to adjust a speed of the host vehicle based on the relative speeds and separation distances between the host vehicle and the detected target vehicles to maintain a safe separation between the host vehicle and the target vehicles.

Abstract: An autonomous vehicle simulation system for analyzing motion planners is disclosed. A particular embodiment includes: receiving map data corresponding to a real world driving environment; obtaining perception data and configuration data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating simulated perception data for each of the plurality of simulated dynamic vehicles based on the map data, the perception data, and the configuration data; receiving vehicle control messages from an autonomous vehicle control system; and simulating the operation and behavior of areal world autonomous vehicle based on the vehicle control messages received from the autonomous vehicle control system.

Abstract: A system and method for adaptive cruise control with proximate vehicle detection are disclosed. The example embodiment can be configured for: receiving input object data from a subsystem of a host vehicle, the input object data including distance data and velocity data relative to detected target vehicles; detecting the presence of any target vehicles within a sensitive zone in front of the host vehicle, to the left of the host vehicle, and to the right of the host vehicle; determining a relative speed and a separation distance between each of the detected target vehicles relative to the host vehicle; and generating a velocity command to adjust a speed of the host vehicle based on the relative speeds and separation distances between the host vehicle and the detected target vehicles to maintain a safe separation between the host vehicle and the target vehicles.

Abstract: A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines that no train is approaching the railroad crossing from the sensor data. The control device detects an indication of a stopped vehicle in front of the autonomous vehicle and behind the railroad crossing. The control device determines whether the stopped vehicle is associated with a mandatory stop rule. The mandatory stop rule indicates vehicles carrying hazardous materials have to stop behind the railroad crossing even when no train is approaching or traveling through the railroad crossing. If it is determined that the stopped vehicle is associated with the mandatory stop rule, the control device waits behind the stopped vehicle until the stopped vehicle crosses the railroad crossing and instructs the autonomous vehicle to cross the railroad.

Abstract: A system for implementing a lane bias maneuver to negotiate a curved road comprises an autonomous vehicle and a control device. The control device determines that the autonomous vehicle is approaching a curved road. The control device determines a road radius of the curved road. The control device calculates a first lane bias adjustment amount associated with a road curvature of the curved road based on the road radius. The control device calculates a second lane bias adjustment amount associated with a trailer angle between a trailer and a semi-truck tractor unit of the autonomous vehicle. The control device calculates a total lane bias adjustment amount by combining the first and second lane bias adjustment amounts. The control device instructs the autonomous vehicle to perform a lane bias maneuver that comprises driving the autonomous vehicle off-center in a curved lane based on the total lane bias adjustment amount.

Abstract: A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines a target lane to travel while crossing the railroad. The target lane is a lane that has available space with at least a length of the autonomous vehicle on the other side of the railroad as opposed to a side of the railroad where the autonomous vehicle is currently traveling. The control device instructs the autonomous vehicle to travel on the target lane. The control device determines that no train is approaching the railroad crossing. The control device instructs the autonomous vehicle to cross the railroad if the target lane still provides available space with at least the length of the autonomous vehicle on the other side of the railroad.

Abstract: A system for implementing a lane bias maneuver to avoid a vehicle comprises an autonomous vehicle and a control device associated with the autonomous vehicle. The control device detects a presence of a vehicle from sensor data revied from sensors of the autonomous vehicle. The control device determines a lateral distance between the autonomous vehicle and the vehicle. The control device compares the lateral distance with a threshold distance. The control device determines whether to instruct the autonomous vehicle to perform a lane bias maneuver based on the comparison between the lateral distance and the threshold distance. The lane bias maneuver comprises driving the autonomous vehicle off-center in a current lane traveled by the autonomous vehicle toward an opposite direction with respect to the vehicle until the lateral distance between the autonomous vehicle and the vehicle is at least equal to the threshold distance.

Abstract: A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines that the railroad crossing is closed from the sensor data. The control device determines one or more re-routing options from map data. The control device determines whether at least one re-routing option reaches a predetermined destination of the autonomous vehicle. In response to determining that the at least one re-routing option reaches the predetermined destination, the control device selects a particular re-routing option based at least on determining that the autonomous vehicle is able to travel according to the particular re-routing option autonomously. The control device instructs the autonomous vehicle to re-route according to the particular re-routing option.

Abstract: A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines a target lane to travel while crossing the railroad. The target lane is a lane that has available space with at least a length of the autonomous vehicle on the other side of the railroad as opposed to a side of the railroad where the autonomous vehicle is currently traveling. The control device instructs the autonomous vehicle to travel on the target lane. The control device determines that a train is approaching the railroad crossing and waits for the train to pass the railroad crossing. The control device determines that the train has passed the railroad crossing. The control device instructs the autonomous vehicle to cross the railroad.