Abstract: A multi-static radar detection system for a vehicle includes one or more receivers for collecting multi-carrier modulation signals emitted by one or more transmitters that are positioned at base stations located in an environment surrounding the vehicle. The multi-carrier modulation signals include one or more types of reference signals. The multi-static radar detection system also includes one or more controllers in electronic communication with the one or more receivers. The one or more controllers execute instructions to collect, by the one or more receivers, a multi-carrier modulation signal emitted by the one or more transmitters. The one or more controllers determine a Doppler shift and a time delay of a respective object located within the environment surrounding the vehicle based on an interpolated time-frequency grid; and transform a value for the Doppler shift into a velocity value and a value for the time delay into a range value.

Abstract: A calibration pipeline for 6DoF alignment parameters for an autonomous vehicle includes an automated driving controller instructed to receive inertial measurement unit (IMU) poses and final radar poses and determine smoothened IMU poses from the IMU poses and smoothened final radar poses from the final radar poses. The automated driving controller aligns the smoothened IMU poses and the smoothened final radar poses with one another to create a plurality of radar-IMU A, B relative pose pairs. The automated riving controller determines a solution yielding a threshold number of inliers of further filtered radar-IMU A, B relative pose pairs, randomly samples the further filtered radar-IMU A, B relative pose pairs with replacements several times to determine a stream of filtered radar-IMU A, B relative pose pairs, and solves for a solution X for the stream of filtered radar-IMU A, B relative pose pairs.

Abstract: An occupant detection system for an interior cabin area of a vehicle includes one or more signal conversion devices including conversion circuitry and a wireless control module including a multi-band transceiver having two or more transceivers. The wireless control module executes instructions to continuously send, by a first transceiver, signals on a first frequency band. The wireless control module executes instructions to receive, by a second transceiver, signals on a second frequency band from a signal conversion device. The wireless control module compares original training symbols from the signals on the first frequency band with training symbols from the signals on the second frequency band to determine a change in value of in one or more channel state information (CSI) parameters, and determines one or more occupants are present within the interior cabin area of the vehicle based on the change in one or more CSI parameters.

Abstract: A system to locate seat controls and belt buckles includes a vehicle having at least one seat. A seat belt system is provided with the at least one seat having a seat belt, a belt latch and a seat belt buckle. A haptic device is provided with the seat belt system, the haptic device generating a haptic signal.

Abstract: An ego vehicle includes a system having one or more input devices for generating an input signal associated with a remote vehicle. The system further includes a computer having one or more processors programmed to receive the input signal from the input device. The processor is further programmed to determine a speed profile of the remote vehicle based on the input signal, determine that the remote vehicle is a traffic impediment based on the input signal, and determine a predicted slowdown event associated with the traffic impediment and the speed profile. The processor is further programmed to generate an actuation signal, in response to the processor determining the speed profile, the traffic impediment, and the predicted slowdown event. The notification device provides the notification of the predicted slowdown event to a user in response to the notification device receiving the actuation signal from the processor.

Abstract: An energy management system for an electric autonomous vehicle comprises a battery and a controller comprising a processor and a non-transitory computer-readable medium. The system comprises a pre-allocation input mechanism transmitting an input signal to the processor relating to a travel destination for the vehicle. The system comprises a navigation module receiving a wireless signal from a satellite network relating to a current location of the vehicle. The system comprises an autonomous input mechanism powered by the battery and transmitting an autonomous signal to the processor relating to dynamic conditions for autonomous operation of the vehicle. The processor determines a route between the current location and the travel destination, performs autonomous operation of the vehicle along the route, and selectively powers or tunes the usage of the at least one autonomous input mechanism with the battery during autonomous operation of the vehicle.

Abstract: A system for providing route guidance based on road brightness metrics includes a primary vehicle having an on-board data processor, a driver interface in communication with the on-board data processor and adapted to allow a driver to interact with the on-board data processor, and a wireless communication module, a cloud-based data processor adapted to collect real-time road brightness data from the primary vehicle and a plurality of other vehicles and to store collected data and build a model of road brightness metrics, the cloud-based data processor further adapted to receive data related to a starting position and final destination, calculate brightness characteristics for a shortest path route and at least one viable alternate route between the starting point and the final destination, and present to a driver of the primary vehicle the at least one viable alternate route which satisfies pre-determined brightness preferences better than the shortest path route.

Abstract: A method of determining the position of a vehicle includes generating a vehicle-based point cloud of objects in proximity to the vehicle, referenced to a vehicle-based coordinate system. The method also includes receiving an infrastructure-based point cloud, referenced to a global coordinate system, of objects detected by a camera mounted at a fixed location external to the vehicle, and registering the vehicle-based point cloud with the infrastructure-based point cloud to determine the vehicle position in the global coordinate system.

Abstract: A communication system that determines a context and an intent of a specific remote vehicle located in a surrounding environment of a host vehicle includes one or more controllers for receiving sensed perception data including sensed perception data. The one or more controllers execute instructions to determine a plurality of vehicle parameters related to the specific remote vehicle. The the one or more controllers execute instructions to associate the specific remote vehicle with a specific lane of travel of a roadway based on map data. The one or more controllers determines possible maneuvers, possible egress lanes, and a speed limit for the specific remote vehicle for the specific lane of travel based on the map data, and determines the context and the intent of the specific remote vehicle based on the plurality of vehicle parameters, the possible maneuvers, the possible egress lanes for the specific remote vehicle, and the speed limit related to the specific remote vehicle.

Abstract: An occupant detection system for determining a unique position of a detected presence within an interior cabin area of a vehicle includes a plurality of signal conversion devices each including conversion circuitry. Each of the plurality of signal conversion devices are assigned to a specific position within the interior cabin area of the vehicle. The conversion circuitry for the plurality of signal conversion devices include unique sub-carrier frequency that corresponds to the unique position within the interior cabin area of the vehicle. The occupant detection system includes a wireless control module including a multi-band transceiver having a first transceiver configured to transmit and receive signals on a first frequency band and a second transceiver configured to transmit and receive signals on a second frequency band.

Abstract: An occupant detection system for an interior cabin area of a vehicle includes one or more signal conversion devices including conversion circuitry and a wireless control module including a multi-band transceiver having two or more transceivers. The wireless control module executes instructions to continuously send, by a first transceiver, signals on a first frequency band. The wireless control module executes instructions to receive, by a second transceiver, signals on a second frequency band from a signal conversion device. The wireless control module compares original training symbols from the signals on the first frequency band with training symbols from the signals on the second frequency band to determine a change in value of in one or more channel state information (CSI) parameters, and determines one or more occupants are present within the interior cabin area of the vehicle based on the change in one or more CSI parameters.

Abstract: A system for associating a physical identity and a virtual identity of a target vehicle includes a data processor, including a wireless communication module, positioned within an ego vehicle, and a plurality of perception sensors, positioned within the ego vehicle and adapted to collect data related to a physical identity of the target vehicle and to communicate the data related to the physical identity of the target vehicle to the data processor via a communication bus, the data processor adapted to receive, via a wireless communication channel, data related to a virtual identity of the target vehicle and to associate the physical identity of the target vehicle with the virtual identity of the target vehicle.

Abstract: An energy management system for an electric autonomous vehicle comprises a battery and a controller comprising a processor and a non-transitory computer-readable medium. The system comprises a pre-allocation input mechanism transmitting an input signal to the processor relating to a travel destination for the vehicle. The system comprises a navigation module receiving a wireless signal from a satellite network relating to a current location of the vehicle. The system comprises an autonomous input mechanism powered by the battery and transmitting an autonomous signal to the processor relating to dynamic conditions for autonomous operation of the vehicle. The processor determines a route between the current location and the travel destination, performs autonomous operation of the vehicle along the route, and selectively powers or tunes the usage of the at least one autonomous input mechanism with the battery during autonomous operation of the vehicle.

Abstract: A scan matching and radar pose estimator for determining a final radar pose for an autonomous vehicle includes an automated driving controller that is instructed to determine a hyper-local submap based on a predefined number of consecutive aggregated filtered data point cloud scans and associated pose estimates. The automated driving controller determines an initial estimated pose by aligning a latest aggregated filtered data point cloud scan with the most recent hyper-local submap based on an iterative closest point (ICP) alignment algorithm. The automated driving controller determines a pose graph based on the most recent hyper-local submap and neighboring radar point cloud scans, and executes a multi-view non-linear ICP algorithm to adjust initial estimated poses corresponding to the neighboring radar point cloud scans in a moving window fashion to determine a locally adjusted pose.

Abstract: A calibration pipeline for 6DoF alignment parameters for an autonomous vehicle includes an automated driving controller instructed to receive inertial measurement unit (IMU) poses and final radar poses and determine smoothened IMU poses from the IMU poses and smoothened final radar poses from the final radar poses. The automated driving controller aligns the smoothened IMU poses and the smoothened final radar poses with one another to create a plurality of radar-IMU A, B relative pose pairs. The automated riving controller determines a solution yielding a threshold number of inliers of further filtered radar-IMU A, B relative pose pairs, randomly samples the further filtered radar-IMU A, B relative pose pairs with replacements several times to determine a stream of filtered radar-IMU A, B relative pose pairs, and solves for a solution X for the stream of filtered radar-IMU A, B relative pose pairs.

Abstract: A scan aggregator and filter for an autonomous vehicle includes a plurality of radar sensors, where each radar sensor performs a plurality of individual scans of a surrounding environment to obtain data in the form of a radar point cloud including a plurality of detection points. The scan aggregator and filter also includes an automated driving controller in electronic communication with the plurality of radar sensors. The automated driving controller is instructed to filter each of the individual scans to define a spatial region of interest and to remove the detection points of the radar point cloud that represent moving objects based on a first outlier-robust model estimation algorithm. The automated driving controller aggregates a predefined number of individual scans together based on a motion compensated aggregation technique to create an aggregated data scan and applies a plurality of density-based clustering algorithms to filter the aggregated data scan.

Abstract: A method of unlocking a vehicle of a user having a handheld device based on visual domain and radio frequency (RF) domain technologies is provided. The method comprises activating an on-board apparatus of the vehicle when the handheld device of the user is within a threshold distance from the vehicle. The method further comprises comparing a visual domain position of the user based on visual domain data with an RF domain position of the user based on RF domain data by a first equation, |dvisualti(X)?dRFti(X)|<?d and a second equation, |?visualti(X)??RFti(X)|<??. The method further comprises validating the user when the first and second equations are true and unlocking the vehicle after the user is validated.

Abstract: An intersection navigation system includes: a complexity module configured to determine a complexity value for an intersection of two or more roads, where the complexity value for the intersection corresponds to a level of complexity for a vehicle to navigate the intersection during autonomous driving; and a driving control module configured to: during autonomous driving of a vehicle, control at least one of: steering of the vehicle; braking of the vehicle; and acceleration of the vehicle; and based on the complexity value of the intersection, selectively adjust at least one aspect of the autonomous driving.

Abstract: An attention-driven streaming system includes an adaptive-streaming module and a transceiver. The adaptive-streaming module includes filters, a compression module, an attention-driven strategy module and a fusion module. The filters filter sensor data received from sensors of a vehicle. The compression module compresses the filtered sensor data to generate compressed data. The attention-driven strategy module generates feedforward information based on a state of the vehicle and a state of an environment of the vehicle to adjust a region of interest. The fusion module generates an adaptive streaming strategy to adaptively adjust operations of each of the filters. The transceiver streams the compressed data to at least one of an edge computing device or a cloud-based network device and in response receive feedback information and pipeline monitoring information.

Abstract: An intersection navigation system includes: a complexity module configured to determine a complexity value for an intersection of two or more roads, where the complexity value for the intersection corresponds to a level of complexity for a vehicle to navigate the intersection during autonomous driving; and a driving control module configured to: during autonomous driving of a vehicle, control at least one of: steering of the vehicle; braking of the vehicle; and acceleration of the vehicle; and based on the complexity value of the intersection, selectively adjust at least one aspect of the autonomous driving.