Abstract: A computer implemented method for determining weights for an attention based trajectory prediction comprises the following steps carried out by computer hardware components: receiving a sequence of a plurality of captures taken by a sensor; determining an unnormalized weight for a first capture of the sequence based on the first capture of the sequence; and determining a normalized weight for the first capture of the sequence based on the unnormalized weight for the first capture of the sequence and a normalized weight for a second capture of the sequence.

Abstract: A computer implemented method for estimating a height profile of a road around a vehicle, comprises the following steps carried out by computer hardware components: receiving radar responses captured by a radar sensor mounted on the vehicle; determining an elevation spectrum of the road around the vehicle based on the radar responses; and estimating the height profile based on the elevation spectrum.

Abstract: This document describes waveguides that use a combination of air dielectric filled channels and non-air dielectric filled channels to obtain beneficial attributes of both air and dielectric waveguides. EM energy loss inside the waveguide compares to a traditional air waveguide. However, with a smaller size than a comparable air waveguide, the example waveguide can occupy less area of a chip or package than a comparable air waveguide of a traditional design. The waveguide has a routing portion with hollow channels filled with an air dielectric. Radiation channels corresponding to each of the hollow channels are loaded with a non-air dielectric. A surface of each of the radiation channels allows EM energy to escape the non-air dielectric. The described waveguide may be particularly advantageous for use in an automotive context, for example, detecting objects in a roadway in a travel path of a vehicle.

Abstract: This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.

Abstract: An illustrative example embodiment of a detector device, which may be useful on an automated vehicle, includes an array of detectors arranged in one dimension. The array includes a plurality of first detectors and a plurality of second detectors. The first detectors respectively have one of the second detectors between the first detector and an adjacent one of the first detectors. The first detectors respectively are spaced from the one of the second detectors by a first distance. The one of the second detectors are respectively spaced from the adjacent one of the first detectors by a second distance that is larger than the first distance. The first detectors are spaced from each other by a third distance that is a sum of the first and second distance. The second detectors are also spaced from each other by the third distance.

Abstract: A computer implemented method for determining a direction of arrival of a radar detection comprises the following steps carried out by computer hardware components: acquiring a complex-valued beamvector of the radar detection; processing the complex-valued beamvector by a machine learning module in the complex domain; and obtaining the direction of arrival as an output of the machine learning module.

Abstract: Communication circuit for bidirectional communication. A switching part is provided including a first BJT having a first base, a first collector, and a first emitter connected to an IO line. The first BJT is activated in response to an input data signal. A transmitter part is provided including a second BJT having a second base connected to the first collector, and a second collector connected to an output. A receiver part is provided comprising a third BJT having a third base connected to an input, and a third collector connected to the first base and to the IO line. A diode is provided in the IO line for impeding current associated with an input data signal and conducting a current associated with an output data signal.

Abstract: A method of detecting occupants within a vehicle includes receiving camera data, thermal data and radar data with respect to the vehicle. The received data is stored to at least a first buffer and a second buffer, wherein the first buffer has a length associated with a first duration of time and the second buffer has a length associated with a second duration of time greater than the first duration of time. The camera data, thermal data, and radar data stored in the respective first and second buffers is analyzed to detect and track camera-based objects, radar-based objects, and thermal-based objects, which are utilized to generate an output indicating whether an occupant was detected.

Abstract: An electrical connector includes a first layer formed of a copper based material and a second layer formed of an iron-nickel alloy. The second layer has a thickness of 8% to 30% of the thickness of the electrical connector. The electrical connector also includes a third layer which is formed of a solder alloy that consists essentially of 17% to 28% indium by weight, 12% to 20% zinc by weight, 1% to 6% silver by weight, 1% to 3% copper by weight, and a remaining weight of the solder alloy that is tin.

Abstract: A computer implemented method for testing a system includes: determining one or more test cases for testing the system, each of the one or more test cases comprising at least one test step; for each of the one or more test cases, carrying out the steps of: executing each of the at least one test step of the respective test case; collecting data generated during and/or after the execution of the at least one test step of the respective test case; and based on a characteristic of the respective test step: evaluating the collected data and setting a status for the at least one test step of the respective test case to passed or failed based on the collected data, or recording the collected data and setting the status for the at least one test step of the respective test case to postponed.

Abstract: A computer system for storing data of driver-assistance systems of vehicles may include: a sensor entity configured to store, for each of at least one sensor mounted on a vehicle, respective sensor configuration data comprising a respective sensor identifier; a vehicle entity configured to store respective vehicle configuration data comprising a respective vehicle identifier and a reference to at least one sensor identifier; a log entity configured to store respective logging configuration data comprising a respective log identifier and a reference to at least one vehicle identifier; a stream entity, wherein the stream entity is configured to store, for each of at least one stream, respective stream configuration data comprising a respective stream identifier, and a reference to at least one log identifier, and a sample entity configured to store respective sample configuration data, comprising a respective sample identifier and a reference to at least one stream identifier.

Abstract: Vehicle movement sensor for a vehicle travelling on a road surface. An optoelectronic sensor from a first sample area on the road surface at a first location relative to the vehicle and generating a pixel pattern image of road surface irregularities in the first sample area. A processor for sampling pixel pattern images of the first sample area and determining movement of the vehicle using digital image correlation of sequential sampled pixel pattern images of the first sample area.

Abstract: Vehicle advanced driver-assistance system (ADAS) techniques include, during an ADAS event where a controller at least partially takes control of the vehicle from a driver, detecting, using at least a driver monitoring system, a driver attentiveness level and a control urgency level indicative of how important it is for the driver to potentially retake control of the vehicle, determining, based on the detected driver attentiveness level and the detected control urgency level, a desired driver alert level for attempting to regain the driver's attention for the driver to potentially retake control of the vehicle from a plurality of predetermined driver alert levels each including at least one of an audio/visual/haptic (AVH) alert and a degree of a braking event, and commanding, according to the desired driver alert level, at least one of an AVH alert system and an anti-lock braking system (ABS).

Abstract: This document describes an integrated vehicle antenna and indicator system. As one example, a recognizable feature of an antenna housing (e.g., a ridge on a shark fin antenna housing) is replaced with an indicator element and transparent cover that are shaped and contoured to preserve the original shape of that recognizable feature of the antenna housing before being replaced. With an indicator element integrated in an antenna housing this way, visual notifications can be output from a highly visible and recognizable indicator mounting position on the vehicle. By outputting a light pattern near a recognizable feature of an external device, such as a shark fin antenna, a visible notification to convey information is provided in a way that does not detract from aesthetics of a vehicle design and is easily recognizable from outside the vehicle.

Abstract: The present disclosure relates to a computerized method of seatbelt verification in a vehicle is presented. The method comprises generating, based on an image showing a seat of the vehicle, at least one heatmap representing probabilities of a seatbelt being present in regions of the image, determining a routing of the seatbelt in the image based on the values of the heatmap, and determining whether the seatbelt is worn correctly based on the routing.

Abstract: Described is a system including a camera module connected to an electronic device. The camera module includes a non-volatile memory that includes instructions storing a script for setting up the camera module. The electronic device includes an interpreter that executes at least partially the instructions including the script stored in the non-volatile memory of the camera module to set up the camera module.

Abstract: A vision system for a motor vehicle includes an optical image capture device configured to capture images of objects in its field of vision. A lighting device includes a plurality of lighting elements forming a lighting matrix configured to illuminate the field of vision of the optical device. A control device associated with the optical device and the lighting device is configured to determine the nature of each image of an object captured by the optical device. The control device is configured to locate the object, select the lighting elements of the lighting matrix capable of illuminating the object relative to the location of the object, and individually control at least one lighting characteristic of each selected lighting element so as to be able to determine the nature of the image of the object captured by the optical device.

Abstract: Techniques are described that enable automatic emergency braking (AEB) for a path-crossing target when a collision between a host vehicle and the target that is deemed imminent. Based on whether an acceleration of the host vehicle is above a threshold. Based on the acceleration, and, optionally, a location of the target relative to a crossing path (e.g., whether a portion of the target is within a suppression zone), an AEB system of the host vehicle is either activated or not activated, for example, suppressed. This suppression of the AEB system may include gating or nulling an AEB activation signal to prevent an emergency braking event. By managing the AEB system in a path-crossing scenario, many common false-positive AEB events (warnings, alerts, and/or braking) may be avoided. Furthermore, intentional vehicle maneuvers that comply with normal driving etiquette or rules can still be allowed for operator and passenger comfort, without risking safety.

Abstract: The present disclosure enables a collection of training data suitable for training an autonomous driving system of a vehicle. A database is provided that stores predefined road scenarios, and user devices are provided with a simulation game for controlling a vehicle agent in a road scenario. A plurality of user devices, running the simulation game, play a road scenario from the database to control a vehicle agent in the simulation game with steering actions entered by individual users, which generates a human demonstration of each road scenario that is played. Several human demonstrations played on the plurality of user devices can be maintained by a demonstration database and made available as training data suitable for training an autonomous driving system of a vehicle. A large amount of training data can be automatically generated, without requiring expensive or time-consuming real-world tests to generate suitable training data manually.

Abstract: A computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.