A sensor assembly for autonomous vehicles includes a side mirror assembly configured to mount to a vehicle. The side mirror assembly includes a first camera having a field of view in a direction opposite a direction of forward travel of the vehicle; a second camera having a field of view in the direction of forward travel of the vehicle; and a third camera having a field of view in a direction substantially perpendicular to the direction of forward travel of the vehicle. The first camera, the second camera, and the third camera are oriented to provide, in combination with a fourth camera configured to be mounted on a roof of the vehicle, an uninterrupted camera field of view from the direction of forward travel of the vehicle to a direction opposite the direction of forward travel of the vehicle.

Systems and methods for monitoring the lane of an object in an environment of an autonomous vehicle are disclosed. The methods include receiving sensor data corresponding to the object, and assigning an instantaneous probability to each of a plurality of lanes based on the sensor data as a measure of likelihood that the object is in that lane at a current time. The methods also include generating a transition matrix for each of the plurality of lanes that encode one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time. The methods then include determining an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix as a measure of likelihood of the object occupying that lane at the current time.

A fast ramp frequency modulated continuous wave (FMCW) radar system (100) is described herein, where the fast ramp FMCW radar system is configured to employ velocity labeled multiplexing (VLM) in connection with generating detections for objects in a scene. Transmitters (110, 112) in the radar system are assigned different velocity labels that corresponds to different phase rates of change of consecutive chirps in signals emitted by the transmitters. Approaches for generating detections based upon echo signals that correspond to the emitted signals are also described herein.

Systems and methods for determining precise pick-up locations for passengers who have requested autonomous vehicle rides. In particular, systems and methods are provided for using wireless signals to determine user location. In some examples, wireless ranging technology, such as Ultra Wide Band (UWB), is used to determine the user location. Wireless transceivers are used to determine a mobile device's range, and range information from multiple transceivers is used to determine the mobile's device's position. In some examples, triangulation is used to determine user location, such as triangulation between one or more wireless transceivers and the mobile device. In various examples, wireless transceivers are installed on autonomous vehicles, and in some examples, wireless transceivers are installed in various static locations (e.g., on buildings, lamp posts, or other structures.

A super resolution system, the system including: at least one antenna; transmission electronics; receiving electronics; and receiving computing electronics, where the transmission electronics are structured to transmit a first electromagnetic wave having an Orbital Angular Momentum wave-front thru the antenna towards a target, where the transmission electronics are structured to transmit a second electromagnetic wave having a non Orbital Angular Momentum wave-front thru a first portion of the antenna towards the target, where the receiving electronics are structured to form a first signal from a first return wave of the first electromagnetic wave, where the receiving electronics are structured to form a second signal from a second return wave of the second electromagnetic wave, and where the receiving computing electronics are structured to compute target information by using at least one difference between the first signal and the second signal.

A method is described which includes receiving a point cloud having a plurality of data points each representing a 3D location in a 3D space, the point cloud being obtained using a detection and ranging (DAR) sensor. For each data point, associating the data point with a 3D volume containing the 3D location of the data point, the 3D volume being defined using a 3D lattice that partitions the 3D space based on spherical coordinates. For at least one 3D volume, the data points are sorted within the 3D volume based on at least one dimension of the 3D lattice; and the sorted data points are stored as a set of ordered data points. The method also includes performing feature extraction on the set of ordered data points to generate a set of ordered feature vectors and providing the set of ordered feature vectors to perform a machine learning inference task.

Disclosed is an obstacle detection system of a vehicle. The obstacle detection system includes a driving information unit configured to calculate driving position information of the vehicle, a determiner configured to anticipate whether or not the vehicle will enter a joining point where the vehicle meets a target road to be joined based on the driving position information calculated by the driving information unit, a sensing unit configured to sense obstacles located beside the vehicle, and a controller configured to change a sensing range of the sensing unit so as to detect an obstacle moving on the target road to be joined, when the determiner anticipates that the vehicle will enter the joining point.

The described aspects and implementations enable fast and accurate object identification in autonomous vehicle (AV) applications by combining radar data with camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar image of a first hypothetical object in an environment of the AV, obtaining a camera image of a second hypothetical object in the environment of the AV, and processing the radar image and the camera image using one or more machine-learning models MLMs to obtain a prediction measure representing a likelihood that the first hypothetical object and the second hypothetical object correspond to a same object in the environment of the AV.

Methods and systems for determining risk associated with operation of autonomous vehicles using autonomous communication are provided. According to certain aspects, autonomous operation features associated with a vehicle may be determined, including features associated with autonomous communication between vehicles or with infrastructure. This information may be used to determine risk levels for a plurality of features, which may be based upon test data regarding the features or actual loss data. Expected use levels and autonomous communication levels may further be determined and used with the risk levels to determine a total risk level associated with operation of the vehicle. The autonomous communication levels may indicate the types of communications, the levels of communication with other vehicles or infrastructure, or the frequency of autonomous communication. The total risk level may be used to determine or adjust aspects of an insurance policy associated with the vehicle.

An apparatus comprises a transmitter that transmits a signal, a receiver, a multiplier, and a signal shifter included in one of the transmitter and the receiver. The receiver receives a reflected signal comprising an interferer signal with at least one of an amplitude noise or a phase noise and generates a baseband signal comprising a real portion and an imaginary portion. The multiplier multiplies the imaginary portion by a value β chosen based on a power difference α between the amplitude noise and the phase noise, resulting in a modified baseband signal. The signal shifter shifts the interferer signal and the modified baseband signal. An estimator can process the reflected signal and estimate a frequency and phase of the interferer signal for the signal shifter. The value β can be represented as: $\beta =\frac{1}{{\alpha }^2}$
where α represents the power difference between the amplitude noise and the phase noise.