Abstract: A vehicle includes a vehicle engine, a steering control unit, an on-board sensor network and a virtual hazard inference 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 virtual hazard inference system has a processor programmed to infer a presence of an occluded object that is not detected by the on-board sensor network. The processor controls the vehicle engine and the steering control unit to perform at least one of a torque control operation and a steering control operation.

Abstract: Activating an engine of a HEV to charge a battery includes obtaining a first GPS trace from a first trip of the HEV along a first route, where the first GPS trace includes first trace metadata; obtaining a second GPS trace from a second trip, where the second GPS trace includes second trace metadata; adding, to a navigation map, an aggregation of the first trace metadata and the second trace metadata for edges of the navigation map; using the navigation map to obtain an activation action of the engine, where the activation action is selected from a set that includes a first activation action of turning the engine on and a second activation action of turning the engine off; and activating the engine according to the activation action, where activating the engine using the first activation action causes the engine to turn on to charge the battery of the HEV.

Abstract: A vehicle data display system includes an electronic display installed within a vehicle, a 3-D sensor and an electronic controller. The 3-D sensor configured to scan areas forward of and along lateral sides of the vehicle producing point cloud. Each data point of the cloud data corresponding to a surface portion of a physical feature. Each data point includes distance, direction and vertical location of each surface point. The electronic controller is connected to the electronic display and the 3-D sensor. The electronic controller receives and evaluates the point cloud from the 3-D sensor generating a 3-D model of detected ones of the physical features around the vehicle including ground surfaces, non-drivable features and driving limiting features relative to the vehicle. The non-drivable features are features that have predetermined geometric relationships with adjacent ground surfaces such that caution is to be taken when driving over or on driving limiting features.

Abstract: A pedal control system for an electric vehicle includes a pedal, a first switch, a second switch, and an electronic controller. The pedal is configured to be operated in a first mode or a second mode. The first mode does not include regenerative braking and the second mode includes regenerative braking. The second mode includes a plurality of sub-modes. The first switch is configured to switch between the first mode and the second mode. The second switch is configured to select one of the plurality of sub-modes when the second mode is selected. The electronic controller is configured to control an operating mode of the pedal when the second mode is selected. Each of the plurality of sub-modes has a different rate of deceleration and regeneration.

Abstract: Proactively mitigating risk to a vehicle traversing a vehicle transportation network is described. First and second hazard zones for first and second objects ahead of the vehicle are respectively determined. The first hazard zone includes a first target lateral constraint that extends over a left lane boundary, and the second hazard zone includes a second target lateral constraint that extends over a right lane boundary. The lateral constraints separately allow the vehicle to avoid the objects without a speed constraint. Where the first and second hazard zones overlap in the longitudinal direction, a lateral buffer is allocated between the lateral constraints to generate first and second allocated lateral constraints. Longitudinal constraints are respectively determined based on times of arrival at each hazard zone. Using the constraints, a proactive trajectory is determined that includes a lateral contingency, a longitudinal contingency, or both. The vehicle is controlled according to the trajectory.

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.

Abstract: A reactive pedal algorithm is used to modify an accelerator pedal output (APO)-to-torque conversion to produce more deceleration for the same accelerator pedal position. Modifying the APO-to-torque conversion provides the driver of a vehicle the sensation that the vehicle is resisting approaching closer to a lead vehicle. The APO-to-torque conversion is modified based on a scene determination to classify vehicles as in-lane, neighbor-lane, or on-coming. Lane change assist methods and systems are used to modify the APO-to-torque conversion range based on a lead vehicle, a neighbor vehicle, or both.

Abstract: A method of controlling an electric vehicle includes operating the vehicle in accordance with a current pedal map. A plurality of alternative pedal maps is defined. A variance for each of the plurality of alternative pedal maps is determined. Operation of the vehicle is switched from the current pedal map to one of the alternative pedal maps based on the determined variance.

Abstract: A vehicle data display system includes an electronic display installed within a vehicle, a 3-D sensor and an electronic controller. The 3-D sensor configured to scan areas forward of and along lateral sides of the vehicle producing point cloud. Each data point of the cloud data corresponding to a surface portion of a physical feature. Each data point includes distance, direction and vertical location of each surface point. The electronic controller is connected to the electronic display and the 3-D sensor. The electronic controller receives and evaluates the point cloud from the 3-D sensor generating a 3-D model of detected ones of the physical features around the vehicle including ground surfaces, non-drivable features and driving limiting features relative to the vehicle. The non-drivable features are features that have predetermined geometric relationships with adjacent ground surfaces such that caution is to he taken when driving over or on driving limiting features.

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: A vehicle lane marking detection system includes a 3D sensor, a driver assist component and an electronic controller. The 3D sensor is installed to a vehicle and is configured to scan physical objects around the vehicle outputting a plurality of data points each corresponding to a surface point of a physical feature. Each data point being defined by distance, direction, intensity and vertical location relative to the vehicle. The electronic controller is connected to the 3D sensor and the driver assist component. The electronic controller evaluates a point cloud defined by the data points identifying lane markings based on the intensity of the data points. The data points having intensities greater than a predetermined level are determined to correspond to lane marking and are provided to the driver assist component with the lane markings for use thereby.

Abstract: An occlusion is identified in a vehicle transportation network. A visibility grid is identified on a second side of the occlusion for a vehicle that is on a first side of the occlusion. The visibility grid is identified with respect to a region of interest that is at least a predefined distance above ground. The visibility grid is used to identify first portions of roads sensed by a sensor positioned on the vehicle and second portions of the roads that are not sensed by the sensor. A driving behavior of the vehicle is altered based on the visibility grid.

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.

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 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 first method includes identifying an occlusion in the vehicle transportation network; identifying, for a first world object that is on a first side of the occlusion, a visibility grid on a second side of the occlusion; and altering a driving behavior of the first vehicle based on the visibility grid. The visibility grid is used in determining whether other world objects exist on the second side of the occlusion. A second includes identifying a first trajectory of a first world object in the vehicle transportation network; identifying a visibility grid of the first world object; identifying, using the visibility grid, a second world object that is invisible to the first world object; and, in response to determining that the first world object is predicted to collide with the second world object, alerting at least one of the first world object or the second world object.

Abstract: Systems and methods for optimizing traffic flow through an intersection are disclosed. In an embodiment, the method includes receiving positional data indicating a current location of a first vehicle intending to pass through the intersection, receiving directional data indicating an intended direction of the first vehicle through the intersection from the current location, determining, based on the positional data and the directional data, whether an intended path of the first vehicle through the intersection interferes with an alternative path through the intersection, and adjusting a traffic signal at the intersection to decrease an amount of time to pass through the intersection via the alternative path.

Abstract: Route planning for a hybrid electric vehicle (HEV) includes obtaining a route between an origin and a destination, where the route is optimized 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, and where the route comprises respective engine activation actions for at least some segments of the route; and controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

Abstract: A system includes a battery, an engine, and a processor. The processor is configured to plan, according to a model, an activation action of the engine of a vehicle for a next road segment subsequent to a current road segment; and activate, for the next road segment, the engine according to the activation action. The model includes a state space that includes a navigation map, which includes the current road segment of the vehicle, 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: Proactively mitigating risk to a vehicle traversing a vehicle transportation network is described. First and second hazard zones for first and second objects ahead of the vehicle are respectively determined. The first hazard zone includes a first target lateral constraint that extends over a left lane boundary, and the second hazard zone includes a second target lateral constraint that extends over a right lane boundary. The lateral constraints separately allow the vehicle to avoid the objects without a speed constraint. Where the first and second hazard zones overlap in the longitudinal direction, a lateral buffer is allocated between the lateral constraints to generate first and second allocated lateral constraints. Longitudinal constraints are respectively determined based on times of arrival at each hazard zone. Using the constraints, a proactive trajectory is determined that includes a lateral contingency, a longitudinal contingency, or both. The vehicle is controlled according to the trajectory.