Abstract: A control subsystem, method, computer program product and autonomous vehicle are provided to respond to an unknown object on a carriageway along which an autonomous vehicle is to travel. In this regard, sensor data is received from at least one vehicle sensor. The sensor data includes location coordinates of the object on the carriageway. The sensor data is evaluated to determine whether the object is known or unknown. If unknown, the size of the object is determined and a motion plan is defined for the autonomous vehicle depending upon the size of the object and the location coordinates of the object relative to the autonomous vehicle. The motion plan that is defined is dependent upon the size of the object with different motion plans being defined for differently sized objects. Driving instructions for the autonomous vehicle are then updated based upon the motion plan that is defined.

Abstract: A system and method for adaptive cruise control with proximate vehicle detection are disclosed. The example embodiment can be configured for: receiving input object data from a subsystem of a host vehicle, the input object data including distance data and velocity data relative to detected target vehicles; detecting the presence of any target vehicles within a sensitive zone in front of the host vehicle, to the left of the host vehicle, and to the right of the host vehicle; determining a relative speed and a separation distance between each of the detected target vehicles relative to the host vehicle; and generating a velocity command to adjust a speed of the host vehicle based on the relative speeds and separation distances between the host vehicle and the detected target vehicles to maintain a safe separation between the host vehicle and the target vehicles.

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: A system and method for vehicle occlusion detection is disclosed.

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: Various embodiments provide a system and method for iterative lane marking localization that may be utilized by autonomous or semi-autonomous vehicles traveling within the lane. In an embodiment, the system comprises a locating device adapted to determine the vehicle's geographic location; a database; a region map; a response map; a plurality of cameras; and a computer connected to the locating device, database, and cameras, wherein the computer is adapted to receive the region map, wherein the region map corresponds to a specified geographic location; generate the response map by receiving information from the camera, the information relating to the environment in which the vehicle is located; identifying lane markers observed by the camera; and plotting identified lane markers on the response map; compare the response map to the region map; and iteratively generate a predicted vehicle location based on the comparison of the response map and the region map.

Abstract: A system and method for taillight signal recognition using a convolutional neural network is disclosed. An example embodiment includes: receiving a plurality of image frames from one or more image-generating devices of an autonomous vehicle; using a single-frame taillight illumination status annotation dataset and a single-frame taillight mask dataset to recognize a taillight illumination status of a proximate vehicle identified in an image frame of the plurality of image frames, the single-frame taillight illumination status annotation dataset including one or more taillight illumination status conditions of a right or left vehicle taillight signal, the single-frame taillight mask dataset including annotations to isolate a taillight region of a vehicle; and using a multi-frame taillight illumination status dataset to recognize a taillight illumination status of the proximate vehicle in multiple image frames of the plurality of image frames, the multiple image frames being in temporal succession.

Abstract: A system comprises an autonomous vehicle (AV) and a control device operably coupled with the AV. The control device detects a series of events within a threshold period of time, where a number of series of events in the series of events is above a threshold number. The series of events taken in the aggregate within the threshold period of time deviates from a normalcy mode. The normalcy mode comprises events that are expected to the encountered by the AV. The control device determines whether the series of events corresponds to a malicious event, where the malicious event indicates tampering with the AV. In response to determining that the series of events corresponds to the malicious event, the series of events are escalated to be addressed.

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: A system and method for three-dimensional (3D) object detection is disclosed. A particular embodiment can be configured to: receive image data from a camera associated with a vehicle, the image data representing an image frame; use a machine learning module to determine at least one pixel coordinate of a two-dimensional (2D) bounding box around an object in the image frame; use the machine learning module to determine at least one vertex of a three-dimensional (3D) bounding box around the object; obtain camera calibration information associated with the camera; and determine 3D attributes of the object using the 3D bounding box and the camera calibration information.

Abstract: Techniques are described to enable a vehicle, such as an autonomous vehicle, to steer and/or apply brakes on a road when a failure condition occurs. An example method for autonomous driving operation includes receiving a reduced set of location information that describes a location of the autonomous vehicle on a road; receiving a reduced set of trajectory information where the autonomous vehicle is expected to be driven; determining a driving path information where the autonomous vehicle is expected to be driven; and in response to determining an occurrence of a fault condition: sending a first instruction to cause the autonomous vehicle to steer the autonomous vehicle using at least the driving path information and the reduced set of location information, and sending a second instruction to cause the autonomous vehicle to apply brakes.

Abstract: Devices, systems and methods for using system-level malfunction indicators to monitor the operation and resiliency of the autonomous driving system components are described. One example of a method for diagnosing a fault in a component of an autonomous vehicle includes receiving, from an electrical sub-component of the component, an electrical signal, receiving, from an electronic sub-component of the component, a message, and determining, based on the electrical signal and the message, an operational status of the component.

Abstract: A system and method for determining car to lane distance is provided. In one aspect, the system includes a camera configured to generate an image, a processor, and a computer-readable memory. The processor is configured to receive the image from the camera, generate a wheel segmentation map representative of one or more wheels detected in the image, and generate a lane segmentation map representative of one or more lanes detected in the image. For at least one of the wheels in the wheel segmentation map, the processor is also configured to determine a distance between the wheel and at least one nearby lane in the lane segmentation map. The processor is further configured to determine a distance between a vehicle in the image and the lane based on the distance between the wheel and the lane.

Abstract: Disclosed are devices and systems for an optimized sensor layout for an autonomous or semi-autonomous vehicle. In one aspect, the system includes a vehicle capable of semi-autonomous or autonomous operation. A plurality of forward-facing cameras is coupled to the vehicle and configured to have a field of view in front of the vehicle. At least three forward-facing cameras of the plurality of forward-facing cameras have different focal lengths. A right-side camera is coupled to the right side of the vehicle, the right-side camera configured to have a field of view to the right of the vehicle. A left-side camera is coupled to the left side of the vehicle, the left-side camera is configured to have a field of view to the left of the vehicle.

Abstract: A method of retrieving a map is disclosed. The method includes receiving a grid data of the map comprising lane segments, wherein the grid data includes an array of grids each associated with a list including none or at least one of the lane segments intersecting the respective grid; receiving coordinates of a location; identifying a first grid including the location based on the grid data; identifying a target grid that has an associated list including at least one of the lane segments as first lane segment; and outputting the first lane segment.

Abstract: Techniques are described to estimate orientation of one or more cameras located on a vehicle. The orientation estimation technique can include obtaining an image from a camera located on a vehicle while the vehicle is being driven on a road, determining, from a terrain map, a location of a landmark located at a distance from a location of the vehicle on the road, determining, in the image, pixel locations of the landmark, selecting one pixel location from the determined pixel locations; and calculating values that describe an orientation of the camera using at least an intrinsic matrix and a previously known extrinsic matrix of the camera, where the intrinsic matrix is characterized based on at least the one pixel location and the location of the landmark.

Abstract: Each of a plurality of landing pads is sized and shaped to accommodate an AV. Landing pad sensor(s) are located in or around each landing pad. A control subsystem receives landing pad sensor data from the one or more landing pad sensors and receives, from a first AV traveling to a location of the plurality of landing pads, a request for an assigned landing pad in which the first AV should park. In response to this request, the control subsystem determines, based on the received landing pad sensor data, whether a first landing pad is free of obstructions that would prevent receipt of the first AV. If the first landing pad is free of obstructions, an indication is provided to the AV that the first landing pad is the assigned landing pad.

Abstract: Disclosed are devices, systems and methods for an audio assistant in an autonomous or semi-autonomous vehicle. In one aspect the informational audio assistant receives a first set of data from a vehicle sensor and identifies an object or condition using the data from the vehicle sensor. Audio is generated representative of a perceived danger of an object or condition. A second set of data from the vehicle sensor subsystem is received and the informational audio assistant determines whether an increased danger exists based on a comparison of the first set of data to the second set of data. The informational audio assistant will apply a sound profile to the generated audio based on the increased danger.

Abstract: Disclosed are methods and devices related to autonomous driving. In one aspect, a method is disclosed. The method includes determining three-dimensional bounding indicators for one or more first objects in road target information captured by a light detection and ranging (LIDAR) sensor; determining camera bounding indicators for one or more second objects in road image information captured by a camera sensor; processing the road image information to generate a camera matrix; determining projected bounding indicators from the camera matrix and the three-dimensional bounding indicators; determining, from the projected bounding indicators and the camera bounding indicators, associations between the one or more first objects and the one or more second objects to generate combined target information; and applying, by the autonomous driving system, the combined target information to produce a vehicle control signal.