Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.

Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.

A measurement and imaging instrument capable of beamforming with high speed and high accuracy without approximate calculation. The instrument includes a reception unit which receives a wave arriving from a measurement object to generate a reception signal; and an instrument main body which performs a lateral modulation while superposing two waves in a two-dimensional case and three or four waves in a three-dimensional case in beamforming processing of the reception signal in which at least one wave arriving from the measurement object is processed as being transmitted or received in the axial direction or directions symmetric with respect to the axial direction to generate a multi-dimensional reception signal, performs Hilbert transform with respect to the multi-dimensional reception signal, and performs partial derivative processing or one-dimensional Fourier transform to generate analytic signals of the multi-dimensional reception signals of the two waves or the three or four waves.

A measurement and imaging instrument capable of beamforming with high speed and high accuracy without approximate calculation. The instrument includes a reception unit which receives a wave arriving from a measurement object to generate a reception signal; and an instrument main body which performs a lateral modulation while superposing two waves in a two-dimensional case and three or four waves in a three-dimensional case in beamforming processing of the reception signal in which at least one wave arriving from the measurement object is processed as being transmitted or received in the axial direction or directions symmetric with respect to the axial direction to generate a multi-dimensional reception signal, performs Hilbert transform with respect to the multi-dimensional reception signal, and performs partial derivative processing or one-dimensional Fourier transform to generate analytic signals of the multi-dimensional reception signals of the two waves or the three or four waves.

The present disclosure relates to devices, systems, and methods relating to configurable silicon photomultiplier (SiPM) devices. An example device includes a substrate and a plurality of single photon avalanche diodes (SPADs) coupled to the substrate. The device also includes a plurality of outputs coupled to the substrate and a plurality of electrical components coupled to the substrate. The plurality of electrical components are configured to selectively connect the plurality of SPADs to the plurality of outputs by selecting which output of the plurality of outputs is connected to each SPAD of the plurality of SPADs and to thereby define a plurality of SiPMs in the device such that each SiPM of the plurality of SiPMs comprises a respective set of one or more SPADs connected to a respective output of the plurality of outputs.

The present disclosure relates to devices, systems, and methods relating to configurable silicon photomultiplier (SiPM) devices. An example device includes a substrate and a plurality of single photon avalanche diodes (SPADs) coupled to the substrate. The device also includes a plurality of outputs coupled to the substrate and a plurality of electrical components coupled to the substrate. The plurality of electrical components are configured to selectively connect the plurality of SPADs to the plurality of outputs by selecting which output of the plurality of outputs is connected to each SPAD of the plurality of SPADs and to thereby define a plurality of SiPMs in the device such that each SiPM of the plurality of SiPMs comprises a respective set of one or more SPADs connected to a respective output of the plurality of outputs.

The subject matter of this specification can be implemented in, among other things, systems and methods that enable lidar channel multiplexing using optical locking of separate lasers while simultaneously imparting different frequency offsets to beams output by different lasers. As a result, received beams reflected from various objects, which are present in an outside environment, can have Doppler-shifted frequencies that do not overlap, facilitating concurrent identification of distances to and velocities of multiple objects.

An ADV includes a method to combine data from multiple sensors. The method compresses video data from a camera to generate compressed video data. The compressed video data are segmented. The method time synchronizes each segment of the compressed video data with data from other sensors. The method then combines each segment of the compressed video data with the corresponding time-synchronized sensor data for the other sensors. In one embodiment, each segment of the compressed video data is independently decodable. In another embodiment, each segment of the compressed video data includes a compressed video unit that is prepended with a buffered portion of the compressed video data that immediately precede the compressed video unit. The length of the compressed video unit is smaller than the length of the independently decodable segment to offer finer granularity in time synchronizing the compressed video data with the other sensor data with a tradeoff.

A device for generating an image of an object by electromagnetic waves has a transmission device which is set up to radiate electromagnetic waves in the direction of the object, a receiving device which is set up to receive electromagnetic waves from the object, and a digital processing and control unit which is set up to generate image data of the object from the measured data. Here, the transmission device and the receiving device are arranged in at least one modular unit. The digital processing and control unit has an interface via which different modular units can be exchangeably coupled to the digital processing and control unit. Here, the interface is set up to transmit data to the modular unit and to receive from this, to transmit control signals to the transmission device and to the receiving device, and to supply the modular unit with energy.

Methods and systems are disclosed for correlating synthetic LiDAR data to a real-world domain for use in training an model for use by autonomous vehicle when operating in an environment. To do this, the system will obtain a data set of synthetic LiDAR data, along with images of a real-world environment. The system will transfer the synthetic LiDAR data to a two-dimensional representation, use the two-dimensional representation and the images to train a model that a vehicle can use to operate in a real-world environment.