Abstract: Described are devices, systems and methods for managing a supplemental brake control system in autonomous vehicles. In some aspects, a supplemental brake management system includes brake control hardware and software that operates with a sensing mechanism for determining the brake operational status and a control mechanism for activating the supplemental brake control in an autonomous vehicle, which can be implemented in addition to the vehicle's primary brake control system.

Abstract: Technique for performing camera calibration on a vehicle is disclosed. A method of performing camera calibration includes emitting, by a laser emitter located on a vehicle and pointed towards a road, a first laser pulse group towards a first location on a road and a second laser pulse group towards a second location on the road, where each laser pulse group includes one or more laser spots. For each laser pulse group: a first set of distances are calculated from a location of a laser receiver to the one or more laser spots, and a second set of distances are determined from an image obtained from a camera, where the second set of distances are from a location of the camera to the one or more laser spots. The method also includes determining two camera calibration parameters of the camera by solving two equations.

Abstract: The present disclosure provides a method, an apparatus and a system for multi-module scheduling, capable of solving at least one of the problems associated with the multi-module scheduling technique in the related art, i.e., inconsistency in data inputted to a computing module, and a significant delay or low throughput in data transmission between computing modules.

Abstract: Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes receiving two sets of values from two sets of sensors, where a first set of sensors measure weights or pressures applied on axles of a vehicle, and where a second set of sensors measure pressures in tires of the vehicle. The method performs an error detection and removal operation to remove or filter out any erroneous values from the two sets of values to obtain two sets of filtered values. The method determines one or more values that describe a weight or pressure applied on the axle to obtain the weight distribution of the vehicle based on the first set of filtered values or the second set of filtered values. Based on the obtained weight distribution of the vehicle, the method can determine a driving operation of the vehicle.

Abstract: Described are devices, systems and methods for managing power generation, storage and/or distribution in autonomous vehicles. In some aspects, a system for power management in an autonomous vehicle having a main power source and one or more alternators includes a vehicle control unit, a secondary power source, and a power management unit. In some embodiments, the power management unit is configured on an autonomous vehicle having a single alternator-charging system for battery charging and battery power control with different battery packs. In some embodiments, the power management unit is configured on an autonomous vehicle having multiple alternator-charging systems for battery charging and battery power control for different battery packs.

Abstract: Devices, systems and methods for fusing scenes from real-time image feeds from on-vehicle cameras in autonomous vehicles to reduce redundancy of the information processed to enable real-time autonomous operation are described. One example of a method for improving perception in an autonomous vehicle includes receiving a plurality of cropped images, wherein each of the plurality of cropped images comprises one or more bounding boxes that correspond to one or more objects in a corresponding cropped image; identifying, based on the metadata in the plurality of cropped images, a first bounding box in a first cropped image and a second bounding box in a second cropped image, wherein the first and second bounding boxes correspond to a common object; and fusing the metadata corresponding to the common object from the first cropped image and the second cropped image to generate an output result for the common object.

Abstract: A system and method for image localization based on semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on an autonomous vehicle; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; identifying extraneous objects in the semantic label image data; removing the extraneous objects from the semantic label image data; comparing the semantic label image data to a baseline semantic label map; and determining a vehicle location of the autonomous vehicle based on information in a matching baseline semantic label map.

Abstract: Techniques are described for transitioning control of a steering system from an autonomous mode in a vehicle to a driver-controlled mode where the driver can control the steering wheel of the steering system. A method includes receiving values that describe an amount of torque and a direction of torque in response to a torque applied to a steering wheel of a steering system operated in an autonomous mode, determining that the values are either greater than or equal to a threshold value or are less than or equal to a negative of the threshold value, determining that the values are measured over a period of time greater than or equal to a pre-determined amount of time, and transitioning the steering system from being operated in the autonomous mode to being operated in a driver-controlled mode in which the steering system is under manual control.

Abstract: Technique for performing camera calibration on a vehicle is disclosed. A method of performing camera calibration includes emitting, by a laser emitter located on a vehicle and pointed towards a road, a first laser pulse group towards a first location on a road and a second laser pulse group towards a second location on the road, where each laser pulse group includes one or more laser spots. For each laser pulse group: a first set of distances are calculated from a location of a laser receiver to the one or more laser spots, and a second set of distances are determined from an image obtained from a camera, where the second set of distances are from a location of the camera to the one or more laser spots. The method also includes determining two camera calibration parameters of the camera by solving two equations.

Abstract: Disclosed are devices, systems and methods for the operational testing on autonomous vehicles. One exemplary method includes configuring a primary vehicular model with an algorithm, calculating one or more trajectories for each of one or more secondary vehicular models that exclude the algorithm, configuring the one or more secondary vehicular models with a corresponding trajectory of the one or more trajectories, generating an updated algorithm based on running a simulation of the primary vehicular model interacting with the one or more secondary vehicular models that conform to the corresponding trajectory in the simulation, and integrating the updated algorithm into an algorithmic unit of the autonomous vehicle.

Abstract: A system and method for actively selecting and labeling images for semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; determining the quality of the semantic label image data based on prediction probabilities associated with regions or portions of the image; and identifying a region or portion of the image for manual labeling if an associated prediction probability is below a pre-determined threshold.

Abstract: A system and method for semantic segmentation using hybrid dilated convolution (HDC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.

Abstract: Disclosed are devices, systems and methods for using a rotating camera for vehicular operation. One example of a method for improving driving includes determining, by a processor in the vehicle, that a trigger has activated, orienting, based on the determining, a single rotating camera towards a direction of interest, and activating a recording functionality of the single rotating camera, where the vehicle comprises the single rotating camera and one or more fixed cameras, and where the single rotating camera provides a redundant functionality for, and consumes less power than, the one or more fixed cameras.

Abstract: A system and method for semantic segmentation using hybrid dilated convolution (HDC) are disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image; performing a convolution operation on the feature map and producing multiple convolution layers; grouping the multiple convolution layers into a plurality of groups; applying different dilation rates for different convolution layers in a single group of the plurality of groups; and applying a same dilation rate setting across all groups of the plurality of groups.

Abstract: A system and method for image localization based on semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on an autonomous vehicle; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; identifying extraneous objects in the semantic label image data; removing the extraneous objects from the semantic label image data; comparing the semantic label image data to a baseline semantic label map; and determining a vehicle location of the autonomous vehicle based on information in a matching baseline semantic label map.

Abstract: Disclosed are devices, systems and methods for capturing an image. In one aspect an electronic camera apparatus includes an image sensor with a plurality of pixel regions. The apparatus further includes an exposure controller. The exposure controller determines, for each of the plurality of pixel regions, a corresponding exposure duration and a corresponding exposure start time. Each pixel region begins to integrate incident light starting at the corresponding exposure start time and continues to integrate light for the corresponding exposure duration. In some example embodiments, at least two of the corresponding exposure durations or at least two of the corresponding exposure start times are different in the image.

Abstract: A system and method for image localization based on semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on an autonomous vehicle; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; identifying extraneous objects in the semantic label image data; removing the extraneous objects from the semantic label image data; comparing the semantic label image data to a baseline semantic label map; and determining a vehicle location of the autonomous vehicle based on information in a matching baseline semantic label map.

Abstract: A system and method for transitioning between an autonomous and manual driving mode based on detection of a driver's capacity to control a vehicle are disclosed. A particular embodiment includes: receiving sensor data related to a vehicle driver's capacity to take manual control of an autonomous vehicle; determining, based on the sensor data, if the driver has the capacity to take manual control of the autonomous vehicle, the determining including prompting the driver to perform an action or provide an input; and outputting a vehicle control transition signal to a vehicle subsystem to cause the vehicle subsystem to take action based on the driver's capacity to take manual control of the autonomous vehicle.

Abstract: The present disclosure provides a method, an apparatus and a system for multi-module scheduling, capable of solving at least one of the problems associated with the multi-module scheduling technique in the related art, i.e., inconsistency in data inputted to a computing module, and a significant delay or low throughput in data transmission between computing modules.

Abstract: The present disclosure provides a method, an apparatus and a system for multi-module scheduling, capable of solving the problem associated with inconsistency in data inputted to a computing module in the multi-module scheduling technique in the related art.