An excess heat-generating element is coupled to a heat sink through a heat conduction path. A thermal switch is mounted in the heat conduction path. A temperature-sensitive element is coupled to the heat conduction path on a same side of the thermal switch as the excess heat-generating element. A temperature monitor is mounted adjacent the temperature-sensitive element. A temperature controller has an input coupled to the temperature output of the temperature monitor and an output control line coupled to an input of the thermal switch. The temperature controller switches off the thermal switch, in response to detecting a temperature below a temperature threshold from the temperature output. When the thermal switch it off, it impedes heat flow from the excess heat-generating element to the heat sink, and the heat flow is redirected to increase heat flow from the excess heat-generating element to the heat-sensitive element.

An excess heat-generating element is coupled to a heat sink through a heat conduction path. A thermal switch is mounted in the heat conduction path. A temperature-sensitive element is coupled to the heat conduction path on a same side of the thermal switch as the excess heat-generating element. A temperature monitor is mounted adjacent the temperature-sensitive element. A temperature controller has an input coupled to the temperature output of the temperature monitor and an output control line coupled to an input of the thermal switch. The temperature controller switches off the thermal switch, in response to detecting a temperature below a temperature threshold from the temperature output. When the thermal switch it off, it impedes heat flow from the excess heat-generating element to the heat sink, and the heat flow is redirected to increase heat flow from the excess heat-generating element to the heat-sensitive element.

Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

A method for calibrating and/or adjusting a lidar system. In the method, in order to perform a measurement-based comparison with respect to an underlying one-dimensionally or two-dimensionally detecting detector unit, a distribution of secondary light incident from the field of view and imaged onto the detector unit, and a center position and/or width of the distribution is/are acquired as position data and compared especially with presumed and/or expected position data featuring an expected center position and/or an expected distribution.

A method and device for identifying contamination on a protective screen of a lidar sensor may involve determining a sector background noise in a particular sector of a detection region of the lidar sensor and a detection region background noise is determined in a remaining detection region or the entire detection region. Contamination in the sector in question is then determined if the sector background noise is significantly lower than the detection region background noise. Alternatively, or additionally, a sector background noise is determined in the sector in question at different sensitivities of a receiver of the lidar sensor, and contamination in the sector in question is then determined if a sector background noise determined with a higher sensitivity is not significantly higher than a sector background noise determined with a lower sensitivity.

A method and device for identifying contamination on a protective screen of a lidar sensor may involve determining a sector background noise in a particular sector of a detection region of the lidar sensor and a detection region background noise is determined in a remaining detection region or the entire detection region. Contamination in the sector in question is then determined if the sector background noise is significantly lower than the detection region background noise. Alternatively, or additionally, a sector background noise is determined in the sector in question at different sensitivities of a receiver of the lidar sensor, and contamination in the sector in question is then determined if a sector background noise determined with a higher sensitivity is not significantly higher than a sector background noise determined with a lower sensitivity.

A laser radar system according to the present invention includes: a light source to output light having a first frequency in a first period and light having a second frequency in a second period; an optical splitter to split the lights, outputted from the light source, into signal light and local oscillator light; an optical modulator to modulate the signal light into pulsed light; an optical antenna to output the pulsed light into space and to receive, as reception light, the scattered light from a target; an optical heterodyne receiver to perform heterodyne detection on the reception light by using the local oscillator light; and a measurement unit to measure the distance to the target or the movement characteristics of the target by using the reception signal detected by the optical heterodyne receiver, wherein the optical heterodyne receiver performs the heterodyne detection on the first frequency of the reception light by using the second frequency of the local oscillator light. With this configuration, a large amount of frequency shift can be provided between the signal light and the local oscillator light, and thus, the distance to the target can be measured with high resolution by using short pulsed-light.

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

A system and method for for determining a degree of point cloud data degradation of a LiDAR sensor of a Self-Driving Car (SDC) using a machine-learning algorithm (MLA) are provided. The method comprises: determining, based on a training point cloud generated by the LiDAR sensor representative of surroundings of the SDC, a plurality of LiDAR features; determining, for each training object in the surroundings, based on statistical data of coverage of training objects with LiDAR points, a plurality of enrichment features; receiving a respective label indicative of a degradation degree of the training point cloud; generating, based on the plurality of LiDAR features, the plurality of enrichment features, and the respective label, a given feature vector of a plurality of feature vectors; training, based on the plurality of feature vectors, the MLA to determine an in-use degree of degradation of in-use sensed data further generated by the LiDAR sensor.