Abstract: Techniques are disclosed for reducing false positives for generating warnings to avoid potential collisions between a vehicle and vulnerable road users (VRUs). This is accomplished via an onboard vehicle safety system that uses crowdsourced map data to determine whether a vehicle is capable of performing a maneuver that results in a lateral shift of the vehicle (which may include a lane-shifting or turning maneuver) within a predetermined threshold time period. The ability for the vehicle to make the turning maneuver, among other driving scenarios, may be used to by the safety system to intelligently determine whether a warning or other action is needed to avoid a potential collision with a VRU. In this way, the occurrence and number of false warnings/interventions are minimized or at least reduced, leading to more attentive drivers and thereby improving VRU safety.

Abstract: Techniques are disclosed to enable the generation of ground truth datasets that identify a continuous time movement trajectory of dynamic objects such as non-stationary vehicles. The techniques as described herein may utilize iterative closest point (ICP) processes to compute a rigid-body transform that describes a 3D correspondence between different pairs of temporal point cloud data sample sets at various discrete time periods that are identified with an object along a movement trajectory. From these rigid-body transforms at each of the discrete time periods, a pose graph optimization may be performed to compute the continuous time movement trajectory. The ground truth datasets may be used to enable training of machine learning models, which may be used by a vehicle computing system when navigating a driving environment.

Abstract: For example, a polarized antenna may include a dielectric layer including an antenna via; a first metal layer on a first side of the dielectric layer, the first metal layer including an antenna feeding trace connected to a first end of the antenna via; and a second metal layer on a second side of the dielectric layer. For example, the second metal layer may include a plurality of antenna elements including a first antenna element, a second antenna element, and a third antenna element. For example, the third antenna element may be connected to a second end of the antenna via. For example, the second metal layer may include a plurality of antenna traces serially connecting the plurality of antenna elements.

Abstract: The disclosed technique is an any-in, first-out (“AIFO”) memory device that improves upon conventional first-in, first-out memory devices. The technique mimics a first-in, first-out queue to provide sequential storage and retrieval in scenarios where transactional ordering cannot be guaranteed. By associating an identifier and a write-pointer in a look-up table with read requests prior to transmission, responses may be received out of order and/or interleaved without delaying processing until the next response in the sequence is received. An internal validity flag may be included in the AIFO memory array to represent the validity of each line in the memory array. Once a read pointer reaches a row that is marked as invalid, the read command pauses and discontinues further reading until a write to the row that marks the row as valid. To further optimize writing, a polarity of the validity may be toggled each read cycle.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.

Abstract: A navigation system may receive a captured image acquired by a camera onboard a host vehicle; determine a localized position of the host vehicle relative to a map representative of the road segment; generate based on the localized position of the host vehicle and three-dimensional location information associated with the plurality of topography features, a two-dimensional image representation of a plurality of road topography features; analyze the captured image and the two-dimensional image representation and determine three-dimensional information for at least one object represented in the captured image, but not represented in the map representative of the road segment, based on the three-dimensional location information associated with the plurality of topography features included in the map; determine, based on the three-dimensional information for the at least one object, at least one navigational action for the host vehicle; and cause the host vehicle to implement the at least one navigational action.

Abstract: A system may receive drive information from each of a plurality of harvesting vehicles that traversed a road segment, wherein the drive information includes at least one indicator of an actual trajectory traveled by the harvesting vehicle and altitude information associated with a road surface included among a plurality of road surfaces associated with the road segment, wherein each of the plurality of road surfaces is located at a different altitude. The system may aggregate the drive information to determine at least one host vehicle drivable path, store a representation of the drivable path, generate a road surface representation for each road surface, store the road surface representations, assign a detected road feature to a road surface representation, and distribute the map to at least one host vehicle navigation system for use in navigating the host vehicle along the road segment relative to the host vehicle drivable path.

Abstract: Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive an image captured by a camera onboard the host vehicle; provide the image to a trained model configured to generate an output identifying two or more target trajectories associated with each of the two or more features represented in the image; determine, based on the output generated by the trained model, location information for the two or more target trajectories associated with each of the two or more features; determine at least one navigational action for the host vehicle based on the location information determined for at least one of the target trajectories; and cause the host vehicle to implement the at least one navigational action.

Abstract: Systems and methods are provided for generating a crowd-sourced map for use in vehicle navigation. In one implementation, a system may include at least one processor configured to receive drive information collected from vehicles that traversed a junction; aggregate the received drive information to determine positions of traffic lights and spline representations for drivable paths; input the determined positions and the spline representations to a trained model configured to generate a traffic light relevancy mapping indicating a traffic light relevancy for traffic light to drivable path pairs of the junction; input an observed vehicle behavior to the at least one trained model to generate an updated traffic light relevancy mapping; store in the crowd-sourced map the indicators of traffic light relevancy for the traffic light to drivable path pairs; and transmit the crowd-sourced map to a vehicle for use in navigating the road segment.

Abstract: A multi-core processor configured to improve processing performance in certain computing contexts is provided. The multi-core processor includes multiple processing cores that implement barrel threading to execute multiple instruction threads in parallel while ensuring that the effects of an idle instruction or thread upon the performance of the processor is minimized. The multiple cores can also share a common data cache, thereby minimizing the need for expensive and complex mechanisms to mitigate inter-cache coherency issues. The barrel-threading can minimize the latency impacts associated with a shared data cache. In some examples, the multi-core processor can also include a serial processor configured to execute single threaded programming code that may not yield satisfactory performance in a processing environment that employs barrel threading.

Abstract: Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processor may be programmed to receive an image captured by a camera; identify an oncoming target vehicle; determine that a planned trajectory for the host vehicle includes a turn that crosses a projected trajectory of the oncoming target vehicle in a potential turn-across-path event; determine a driving jurisdiction associated with the road; determine, based on the driving jurisdiction, whether the target vehicle is approaching a road feature that negates the potential turn-across-path event; and either implement or forego implementing a remedial action based on whether the target vehicle is approaching a road feature that negates the potential turn-across-path event.

Abstract: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for predicting a driver path of a vehicle. In the method, an image of a driver of the vehicle is received. Based on the image, a gaze direction of the driver is determined. Based at least in part on the gaze direction, a path of the vehicle is predicted. The vehicle is controlled based on the predicted path.

Abstract: For example, a compiler may be configured to compile a source code into a target code configured for execution by a target processor in a plurality of execution cycles including a first execution cycle, a second execution cycle after the first execution cycle, and a third execution cycle after the second execution cycle. For example, the target code may include one or more no operation (no-op) instructions configured to maintain a first variable live such that a value of the first variable is to be available in a register of the target processor at the first execution cycle and at the third execution cycle.

Abstract: A driver assistance system may also include a driver monitoring system that determines driver awareness. The driver monitoring system may include a camera that observes the driver. According to an embodiment, based on an image captured from the camera, a gaze direction of the driver may be determined. The gaze direction may be evaluated to determine whether it is directed at an object from the vehicle surroundings. If the object is within the driver trajectory, a driver assistance system may recognize that the driver is aware of the object. For that reason, the driver assistance system may delay automatic braking that would otherwise be triggered due to the object.

Abstract: A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one vehicle-induced occlusion zone in an environment of the host vehicle; and cause a navigational change for the host vehicle based, at least in part, on a size of a target vehicle that induces the identified occlusion zone.

Abstract: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for improving object detection based on gaze direction. In the method, a first image of surroundings of a vehicle is received. A second image of a driver of the vehicle is also received. The second image captured contemporaneously with the first image. Based on the second image, a gaze direction of the driver is determined. Based on the gaze direction, a region of the first image being viewed by the driver is determined. An object detection algorithm is applied to recognize an object in the surroundings of the vehicle at a greater level of detail within the region.

Abstract: Systems and methods use cameras to provide autonomous navigation features. In one implementation, a driver-assist object detection system is provided for a vehicle. One or more processing devices associated with the system receive at least two images from a plurality of captured images via a data interface. The device(s) analyze the first image and at least a second image to determine a reference plane corresponding to the roadway the vehicle is traveling on. The processing device(s) locate a target object in the first two images, and determine a difference in a size of at least one dimension of the target object between the two images. The system may use the difference in size to determine a height of the object. Further, the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold.

Abstract: Embodiments here observe the driver and determine an orientation of an articulating camera based on driver gaze tracking and external object detection. A fixed camera collects image data of the vehicle's surroundings, and an articulating camera collects image data of the driver. The gaze direction is used to identify what objects in the vehicle's surroundings the driver is looking at. Based on the position of the object, an orientation of the articulating camera may be determined.

Abstract: For example, a compiler may be configured to identify a loop nest based on a source code to be compiled into a target code to be executed by a target processor, the loop nest including a plurality of loops including at least a first loop and a second loop nested in the first loop, the first loop including at least one first-loop instruction outside the second loop; and to generate Address Generation Unit (AGU) configuration code to configure an AGU of the target processor based on the first-loop instruction, wherein the AGU configuration code is to configure a first dimension of the AGU based on the first loop and a second dimension of the AGU based on the second loop to configure a memory-access operation based on the first-loop instruction, to be performed at a start of the second loop or at an end of the second loop.

Abstract: A driver assistance system may also include a driver monitoring system that determines driver awareness. The driver monitoring system may include a camera that observes the driver. Embodiments avoid unnecessary lane keeping activity based on where the driver is gazing. For example, if the driver is looking at an adjacent lane, then lane keeping activity to avoid or discourage the vehicle from moving into the adjacent lane may be disabled. And vice-versa, if the vehicle is shifting to adjacent lane in one direction and the driver is gazing at the opposite side, then the lane keeping activity, or at least warning, could be activated earlier.