Abstract: Example metal bridge detection systems and methods are described. In one implementation, a method receives LIDAR data from a LIDAR system mounted to a vehicle and receives camera data from a camera system mounted to the vehicle. The method analyzes the received LIDAR data and the camera data to identify a metal bridge proximate the vehicle. If a metal bridge is identified, the method adjusts vehicle operations to improve vehicle control as it drives across the metal bridge.

Abstract: A method for improving the safety and comfort of a vehicle driving over a railroad track, cattle guard, or the like. The method may include receiving, by a computer system, one or more inputs corresponding to one or more forward looking sensors. The computer system may also receive data characterizing a motion of the vehicle. The computer system may estimate, based on the one or more inputs and the data, a motion of a vehicle with respect to a railroad track, cattle guard, or the like extending across a road ahead of the vehicle. Accordingly, the computer system may change a suspension setting, steering setting, or the like of the vehicle to more safely or comfortably drive over the railroad track, cattle guard, or the like.

Abstract: A method for automatically detecting and safely traversing an accumulation of ice on an impending bridge. The method automatically identifies, by the vehicle, impending approach of the vehicle to a bridge and senses an accumulation of ice on the bridge. The method then calculates a speed of the vehicle needed to prevent longitudinal slip between the vehicle and the bridge, and automatically slows the vehicle at a rate sufficient to enable the vehicle to reach the calculated speed by the time it reaches the bridge. A corresponding system is also disclosed and claimed herein.

Abstract: The present invention extends to methods, systems, and computer program products for distinguishing roadway surface lane markings for a vehicle to follow. Automated driving or driving assist vehicles utilize sensors to help the vehicle navigate on roadways or in parking areas. The sensors can utilize, for example, the painted surface markings to help guide the vehicle on its path. When ambiguity is detected between roadway surface markings, decision making algorithms identify a set of roadway surface markings for a vehicle to abide by. The sensors can also identify the location and trajectory of neighboring vehicles to increase confidence with respect to an identified set of roadway surface markings.

Abstract: The present invention extends to methods, systems, and computer program products for using virtual data to test and train parking space detection systems. Aspects of the invention integrate a virtual driving environment with sensor models (e.g., of a radar system) to provide virtual radar data in relatively large quantities in a relatively short amount of time. The sensor models perceive values for relevant parameters of a training data set. Relevant parameters can be randomized in the recorded data to ensure a diverse training data set with minimal bias. Since the driving environment is virtualized, the training data set can be generated alongside ground truth data. The ground truth data is used to annotate true locations, which are used to train a parking space classification algorithms to detect the free space boundaries.

Abstract: The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as bollard receivers. A method for generating virtual sensor data includes simulating a 3-dimensional (3D) environment that includes one or more objects, such as bollard receivers. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions. The ground truth includes information about at least one bollard receiver within the sensor data. For example, the ground truth may include a height of the at least one of the parking barriers. The method also includes storing and associating the virtual sensor data and the virtual ground truth.

Abstract: A method for generating training data is disclosed. The method may include executing a simulation process. The simulation process may include traversing a virtual, forward-looking sensor over a virtual road surface defining at least one virtual railroad crossing. During the traversing, the virtual sensor may be moved with respect to the virtual road surface as dictated by a vehicle-motion model modeling motion of a vehicle driving on the virtual road surface while carrying the virtual sensor. Virtual sensor data characterizing the virtual road surface may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the road surface in the real world.

Abstract: The present invention extends to methods, systems, and computer program products for distinguishing lane markings for a vehicle to follow. Automated driving or driving assist vehicles utilize sensors to help the vehicle navigate on roadways or in parking areas. The sensors can utilize, for example, the painted surface markings to help guide the vehicle on its path. Aspects of the invention use a first type of sensor and at least a second different type of sensor to identify road surface markings. When ambiguity is detected between road surface markings, decision making algorithms identify the correct set of markings for a vehicle to abide by. The sensors also identify the location and trajectory of neighboring vehicles to increase confidence with respect to the identified road-surface markings.

Abstract: The present invention extends to methods, systems, and computer program products for detecting available parking spaces in a parking environment. Radar systems are utilized to gather data about a parking lot environment. The radar data is provided to a neural network model as an input. Algorithms employing neural networks can be trained to recognize parked vehicles and conflicting data regarding debris, shopping carts, street lamps, traffic signs, pedestrians, etc. The neural network model processes the radar data to estimate parking space boundaries and to approximate the parking space boundaries as splines. The neural network model outputs spline estimations to a vehicle computer system. The vehicle computer system utilizes the spline estimates to detect available parking spaces. The spline estimates are updated as the vehicle navigates the parking environment.

Abstract: A method for improving the safety and comfort of a vehicle driving over a railroad track, cattle guard, or the like. The method may include receiving, by a computer system, one or more inputs corresponding to one or more forward looking sensors. The computer system may also receive data characterizing a motion of the vehicle. The computer system may estimate, based on the one or more inputs and the data, a motion of a vehicle with respect to a railroad track, cattle guard, or the like extending across a road ahead of the vehicle. Accordingly, the computer system may change a suspension setting, steering setting, or the like of the vehicle to more safely or comfortably drive over the railroad track, cattle guard, or the like.

Abstract: The disclosure relates generally to methods, systems, and apparatuses for automated or assisted driving and more particularly relates to identification, localization, and navigation with respect to bollard receivers. A method for detecting bollard receivers includes receiving perception data from one or more perception sensors of a vehicle. The method includes determining, based on the perception data, a location of one or more bollard receivers in relation to a body of the vehicle. The method also includes providing an indication of the location of the one or more bollard receivers to one or more of a driver and component or system that makes driving maneuver decisions.

Abstract: Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

Abstract: The present invention extends to methods, systems, and computer program products for distinguishing lane markings for a vehicle to follow. Automated driving or driving assist vehicles utilize sensors to help the vehicle navigate on roadways or in parking areas. The sensors can utilize, for example, the painted surface markings to help guide the vehicle on its path. Aspects of the invention use a first type of sensor and at least a second different type of sensor to identify road surface markings. When ambiguity is detected between road surface markings, decision making algorithms identify the correct set of markings for a vehicle to abide by. The sensors also identify the location and trajectory of neighboring vehicles to increase confidence with respect to the identified road-surface markings.

Abstract: Systems, methods, and devices for sensor fusion are disclosed herein. A system for sensor fusion includes one or more sensors, a model component, and an inference component. The model component is configured to calculate values in a joint-probabilistic graphical model based on the sensor data. The graphical model includes nodes corresponding to random variables and edges indicating correlations between the nodes. The inference component is configured to detect and track obstacles near a vehicle based on the sensor data and the model using a weighted-integrals-and-sums-by-hashing (WISH) algorithm.

Abstract: Methods and apparatus pertaining to a testbed for lane boundary detection in a virtual driving environment are provided. A method may involve generating, by a processor, a virtual driving environment comprising one or more driving lanes, a virtual vehicle, and one or more virtual sensors mounted on the virtual vehicle configured to generate simulated data as the virtual vehicle traverses within the virtual environment. The method may also involve executing an algorithm to process the simulated data to detect the one or more driving lanes. The method may further involve recording an output of the algorithm. The method may additionally involve annotating the simulated data with the output of the algorithm.

Abstract: A method and an apparatus pertaining to generating training data. The method may include executing a simulation process. The simulation process may include traversing one or more virtual sensors over a virtual driving environment defining a plurality of lane markings or virtual objects that are each sensible by the one or more virtual sensors. During the traversing, each of the one or more virtual sensors may be moved with respect to the virtual driving environment as dictated by a vehicle-dynamic model modeling motion of a vehicle driving on a virtual road surface of the virtual driving environment while carrying the one or more virtual sensors. Virtual sensor data characterizing the virtual driving environment may be recorded. The virtual sensor data may correspond to what an actual sensor would produce in a real-world environment that is similar or substantially matching the virtual driving environment.

Abstract: A computing device includes a processing circuit and a data storage medium, and is programmed to receive a user input representing a vehicle control action associated with operating a virtual vehicle in a virtual environment, virtually navigate the virtual vehicle through the virtual environment according to the vehicle control action, collect virtual sensor data, and process the virtual sensor data collected.

Abstract: A computing device comprising a processing circuit and a data storage medium. The computing device is programmed to receive virtual sensor data that represents data collected by a virtual sensor associated with autonomously operating a virtual vehicle in a virtual environment and process the virtual sensor data to identify a limitation of a real-world sensor.