Abstract: A system may receive a video stream from a camera of a vehicle in a transportation network. The system may also receive an input indicating an acceleration and a steering angle of a simulated lead vehicle. The system may determine a pose of the simulated vehicle relative to a home pose based on the acceleration input and the steering input, and display an overlay of a representation of the simulated vehicle in the video stream at pixel coordinates based on the pose. The system may determine, from the pixel coordinates, spatial coordinates of the simulated vehicle in the transportation network, wherein the spatial coordinates are relative to at least one of the transportation network or the camera. The system may transmit the spatial coordinates to the vehicle to cause the vehicle to follow a path based on the spatial coordinates.

Abstract: Determining a speed plan for an autonomous vehicle (AV) is disclosed. Planned locations of the AV for future time steps are placed in an occupancy grid. The planned locations are based on a strategic speed plan that is determined without taking world objects into account. Predicted locations of the world objects for at least some of the future time steps are placed in the occupancy grid. Respective buffer distances corresponding to the predicted locations are added in the occupancy grid. An estimated speed plan is identified for the AV based on the occupancy grid. The speed plan is obtained from the estimated speed plan. The AV is then controlled according to the speed plan.

Abstract: Auto-deceleration of an electric vehicle (EV) is described. A stop line ahead of the EV is detected using map data or sensor inputs. A deceleration plan is generated for the EV. The deceleration plan is based on at least a distance to the stop line, a current speed of the EV. The deceleration plan ends at the stop line. A full release of an accelerator pedal by a driver is detected. Using regenerative braking or coast torque adjustments, acceleration or deceleration of the EV is adjusted based on the deceleration plan.

Abstract: A vehicle drivable area detection system includes a vehicle, at least one 3D sensor and an electronic controller. The at least one 3D sensor is installed to the vehicle and is configured to scan and capture data using laser imaging, detection and distance ranging relative to the vehicle. The data collected represents ground surface features including vertical obstacles, non-vertical obstacles and a drivable area proximate the vehicle within a line-of-sight of the 3D sensor. The electronic controller is installed within the vehicle and is electronically connected to the 3D sensor and at least one driver assist component. The electronic controller conducts the following: a vertical obstacle extraction from the data; terrain estimating from the data; curb detection from the data; and generating a plurality of data elements identifying vertical obstacles including curbs and the drivable area to the at least one driver assist component.

Abstract: At least one virtual road user is generated, wherein a position of a respective virtual road user of the at least one virtual road user corresponds to a border of a range of a sensor of a host vehicle approaching an intersection of a vehicle transportation network. A most relevant virtual road user of the at least one virtual road user is determined, the most relevant virtual road user being associated with an earliest crossing lane of the intersection from a perspective of the host vehicle. A time to contact for the most relevant virtual road user is determined, wherein the time to contact is based on an acceleration of the host vehicle, a predicted trajectory of the most relevant virtual road user, and a relative distance between the host vehicle and the most relevant virtual road user. A target speed for the host vehicle is determined based on the time to contact and the relative distance. The host vehicle is operated using the target speed as input to a control system of the host vehicle.

Abstract: A method includes receiving information about vehicles traveling within a vehicle transportation network. The information comprises driveline data and information regarding lanes within the network. The driveline data comprises one or more drivelines representing a position of one of the vehicles as the vehicle traverses the network. The method also includes consolidating all of the information as raw data, delineating an intersection area from the information about the vehicle transportation network, constructing way data from the information about the network, storing intermediate data comprising the information, the intersection area, and the way data, estimating a number of the lanes within the network based on the way data, and inferring a drive location with the lanes based on the way data. The method also includes creating a map that includes the lanes, the drive location within the lanes, and the intersection area within the network.

Abstract: An apparatus of a vehicle with a processor. The processor is configured to generate at least one landmark within a vehicle transportation network that includes a lane. The processor generates lane cues for the lane as the vehicle travels within the vehicle transportation network. The processor aligns the lane cues. The processor generates a lane graph estimation based upon the lane cues so that the vehicle travels substantially along a center of the lane within the vehicle transportation network. The processor compares the center of the lane generated by the lane graph estimation to the at least one landmark to check a location of the center of the lane. The vehicle is at least temporarily free of communication with a global positioning satellite (GPS), a global navigation satellite system (GNSS), or both as the vehicle travels within the vehicle transportation network and generates the lane cues.

Abstract: A vehicle includes a vehicle engine, a steering control unit, an on-board sensor network and a navigational constraint control system. The vehicle engine generates a torque output of the vehicle. The steering control unit controls a steering angle of the vehicle. The on-board sensor network is programmed to detect external objects within a detection zone. The navigational constraint control system has a memory for storing a path index for the vehicle's navigation. The processor is programmed to determine a reference trajectory from the path index. The processor is further programmed to calculate navigational constraints for the determined reference trajectory to determine a nominal trajectory based on information detected by the on-board sensor network. The processor is programmed to control at least one of the vehicle engine and the steering control unit in accordance with the nominal trajectory.

Abstract: A method includes obtaining a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle and obtaining a reference path associated with the autonomous vehicle. The reference path is based on a drive plan and driveline data. The method includes accumulating slow crossing hazard (SCH) data in the evidence grid by performing at least one of a boundary intrusion detection procedure, an object intrusion detection procedure, or a lead vehicle hazard detection procedure. The SCH data is provided to a process within an autonomous vehicle for further decision-making or risk mitigation.

Abstract: Determining a speed plan for an autonomous vehicle (AV) is disclosed. A path of the AV in a lane of a road is predicted. A path of an other road user (ORU) vehicle in the lane of the road is also predicted. An interaction time between the AV and the ORU vehicle is determined. A classification of the ORU vehicle is determined based on the interaction time and the predicted path of the AV. A speed of the AV is established based on the classification of the ORU vehicle.

Abstract: A real-time accelerator pedal output of the vehicle that quantifies the extent to which the accelerator pedal is pressed by a driver over a period of time is determined. Whether a real-time speed of the vehicle is within a predefined speed range associated with a type of road being traversed is determined. Responsive to the real-time speed being within the predefined speed range, a target mean accelerator pedal output is identified based on the road type and pedal-pressing behaviors of multiple reference drivers associated with the road type. The torque of the vehicle is adjusted based on the target mean accelerator pedal output.

Abstract: An evidence grid is established in a map frame based on the vehicle's pose. Hazard data is accumulated in the evidence grid by gathering visible grid cells, each associated with a visibility probability indicating a history of visibility determinations, and accumulating hazard presence, with each grid cell linked to a hazard associated with a hazard presence probability indicating a history of hazard presence determinations. The accumulated hazard data is then provided to a process within the autonomous vehicle for further decision-making or risk mitigation.

Abstract: Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

Abstract: A mean of real-time accelerator pedal output of a vehicle that quantifies an extent to which an accelerator pedal has been pressed by a driver of the vehicle over a defined period of time is determined. Target mean accelerator pedal output for the vehicle is determined. Torque of the vehicle is changed. The torque is reduced when the mean of the real-time accelerator pedal output is lower than the target mean accelerator pedal output, and the torque is increased when the mean of the real-time accelerator pedal output is higher than the target mean accelerator pedal output.

Abstract: Observed driveline mean and variance data are used for determining the variance of a trajectory of tracked objects for use by a host vehicle. A map of a portion of a vehicle transportation network are determined, wherein the map is comprised of observed driveline mean and variance data for one or more map points. At least one trajectory of a tracked object is predicted, wherein a trajectory includes a series of location each corresponding to a respective predicted position of the tracked object at a future time. A map-based variance is generated for the location of the trajectory using a smoothed curvature of the trajectory within the map. A control system of the vehicle operates the vehicle using the map-based variance as input.

Abstract: Trajectory planning include identifying state data based on sensor data from sensors of a vehicle. The state data are input to a machine-learning model to obtain parameters of a trajectory planner. The parameters are input to the trajectory planner to obtain a short term speed plan. The vehicle is autonomously controlled according to at least a portion of the short term speed plan.

Abstract: Engine activation planning in a hybrid electric vehicle (HEV) is disclosed. Historical driving data of the HEV are analyzed to identify recurring driving scenarios and patterns specific to a driver of the HEV. Future driving scenarios are predicted based on the historical driving data. An engine activation policy is generated for the HEV. The engine activation policy optimizes battery charging and usage in response to the future driving scenarios. The engine activation policy is used to control activation of a gasoline engine in the HEV where a control decision is dynamically adjusted based on a comparison of real-time driving conditions with the future driving scenarios.

Abstract: A method for planning an activation action for an engine of a vehicle is disclosed. The method includes planning, according to a model, an activation action of an engine of a vehicle, and activating the engine according to the activation action. The model includes a state space comprising a current charge level of the battery and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

Abstract: A track based moving object association is described for distributed sensing applications, such as for identifying multiple objects within a vehicle transportation network for use by a vehicle navigating the network. Position information for the multiple objects within a portion of the vehicle transportation network is obtained from multiple sensors within the portion of the vehicle transportation network. Using the position information of respective sensors of the multiple sensors, a respective track for objects of the multiple objects is determined. Similarity measures are determined for multiple tracks, including at least a first track determined using the position information of a first sensor of the multiple sensors and a second track determined using the position information of a second sensor of the multiple sensors. Based on the similarity measures of the tracks, a tracked object track for a tracked object of the multiple objects is determined.

Abstract: A proactive pedal algorithm is used to modify an accelerator pedal map to ensure the deceleration when the accelerator pedal is released matches driver expectation. Modifying the accelerator pedal map provides the driver of a vehicle the sensation that the vehicle resists moving when travelling in dense scenes with potentially high deceleration requirements and coasts easily in scenes with low deceleration requirements. The accelerator pedal map is modified based on a scene determination to classify other remote vehicles as in-lane, neighbor-lane, or on-coming.