Abstract: Techniques for charging a battery associated with a vehicle are discussed herein. A dense charging station for charging the battery may have lanes arranged in parallel, and each of the lanes may have sequential charging locations. A vehicle utilizing the charging station may position itself at the first available charging location, being receiving energy, and determine if a subsequent charging station becomes available in the lane, and then position itself at the subsequent charging location, once available. Multiple charging stations may be required to maintain a threshold power state for individual vehicles in a fleet of vehicles providing a service for a geographic region. A charging coordinator may determine when a battery of a vehicle does not satisfy a threshold power state and requires a recharge. Additionally, the charging coordinator may determine a candidate charging station from among multiple charging stations associated with the geographic region.

Abstract: Brake force distribution during an anti-lock braking (ABS) event in a vehicle using an electronic parking brake (EPB) force to supplement a primary brake pressure is described. In an example, an vehicle control system of the vehicle can detect when an ABS event is going to occur and a primary brake pressure can be applied to a rotor at a wheel of the vehicle using a primary brake system. An EPB brake pressure can be applied to the rotor using an EPB brake system to reduce the pressure required to be applied by the primary brake pressure. As a result, the primary brake system may be sized to accommodate normal braking and to accommodate ABS braking when supplemented by the EPB system.

Abstract: A vehicle computer system implements techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques include using a model to determine a first object trajectory for an object (e.g., a predicted object trajectory) and/or a potential object in an occluded area, as well as a second object trajectory for the object or potential object (e.g., an adverse object trajectory). The model is configured to use one or more algorithms, classifiers, and/or computational resources to predict candidate trajectories for the vehicle based on at least one of the first object trajectory or the second object trajectory. Based on the predicted behavior of the object (or potential object) and the predicted candidate trajectories for the vehicle, a vehicle computer system controls operation of the vehicle.

Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.

Abstract: Techniques for determining to modify a trajectory based on an object are discussed herein. A vehicle can determine a drivable area of an environment, capture sensor data representing an object in the environment, and perform a spot check to determine whether or not to modify a trajectory. Such a spot check may include processing to incorporate an actual or predicted extent of the object into the drivable area to modify the drivable area. A distance between a reference trajectory and the object can be determined at discrete points along the reference trajectory, and based on a cost, distance, or intersection associated with the trajectory and the modified area, the vehicle can modify its trajectory. One trajectory modification includes following, which may include varying a longitudinal control of the vehicle, for example, to maintain a relative distance and velocity between the vehicle and the object.

Abstract: Apparatus and methods for determining an atmospheric condition in an environment of a vehicle are described herein. A long wave infrared camera may be used to produce thermal image data from a field of view of the LWIR camera that includes an unknown atmospheric condition. The thermal image data may include a characteristic that may be compared to characteristic of thermal image data for a known atmospheric condition. A result of the comparison may be used to make a determination related to the unknown atmospheric condition which may be used in controlling the vehicle.

Abstract: Techniques are discussed for determining reflected returns in radar sensor data. In some instances, pairs of radar returns may be compared to one another. For example, a reflection point may be determined from a first position of a first radar return and a second position of a second radar return. Additional data, e.g., sensor data and/or map data, may be used to determine the presence of objects in the environment. The first return or the second return may be a reflected return if an object is disposed at the reflection point. In some instances, a vehicle, such as an autonomous vehicle, may be controlled at the exclusion of information from reflected returns.

Abstract: Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

Abstract: Techniques for determining an object contour are discussed. Depth data associated with an object may be received. The depth data, such as lidar data, can be projected onto a two-dimensional plane. A first convex hull may be determined based on the projected lidar data. The first convex hull may include a plurality of boundary edges. A longest boundary edge, having a first endpoint and a second endpoint, can be determined. An angle can be determined based on the first endpoint, the second endpoint, and an interior point in the interior of the first convex hull. The longest boundary edge may be replaced with a first segment based on the first endpoint and the interior point, and a second segment based on the interior point and the second endpoint. An updated convex hull can be determined based on the first segment and the second segment.

Abstract: Model-based control of dynamical systems typically requires accurate domain-specific knowledge and specifications system components. Generally, steering actuator dynamics can be difficult to model due to, for example, an integrated power steering control module, proprietary black box controls, etc. Further, it is difficult to capture the complex interplay of non-linear interactions, such as power steering, tire forces, etc. with sufficient accuracy. To overcome this limitation, a recurring neural network can be employed to model the steering dynamics of an autonomous vehicle. The resulting model can be used to generate feedforward steering commands for embedded control. Such a neural network model can be automatically generated with less domain-specific knowledge, can predict steering dynamics more accurately, and perform comparably to a high-fidelity first principle model when used for controlling the steering system of a self-driving vehicle.

Abstract: Techniques relating to performance-based metrics for evaluating system quality are described. In an example, sensor data associated with an environment within which a vehicle is positioned is received. A performance metric associated with a trajectory, indicative of a performed behavior of the vehicle, can be determined and a correctness metric can be determined based at least in part on the performance metric. The correctness metric can represent a correctness of the performed behavior. Modification(s) to component(s) and/or system(s), or portions thereof, of the vehicle can be affected based at least in part on the correctness metric.

Abstract: Driving simulations may be generated based on driving log data and used to validate autonomous vehicle safety systems. A driving simulation system may receive driving log data from autonomous vehicles and determine events which caused a vehicle to diverge from a planned trajectory. Driving simulations may be generated based on the driving log data, and executed using a simulated vehicle that follows the initial planned trajectory of the autonomous vehicle. The driving simulations may be analyzed to detect potential collisions and near misses, and to determine success conditions for the behaviors of vehicle safety systems. Driving simulations and associated success conditions may be aggregated and used in validation suites for vehicle controllers and/or vehicle safety systems, thereby providing more comprehensive and robust autonomous vehicle validation based on real-world driving scenarios.

Abstract: This disclosure describes techniques for detecting multipath radar returns and modifying radar data. A vehicle may use radar devices to receive radar data while traversing within an environment. The vehicle may process the radar data using a virtual array based on an arraignment of the antennae within an aperture of the radar device. Using the virtual array, the vehicle may determine an elevated noise level that may be indicative of a multipath radar return. Based on the elevated noise level, the vehicle may determine a second virtual array associated with multipath radar returns, and may process the radar data using the second virtual array. Based on determining that the noise level associated with the second virtual array is lower than the initial noise level, the vehicle may determine that the radar data includes a multipath radar return, and may modify the radar data to correct or mitigate the error caused by the multipath return.

Abstract: An occupant protection system for a vehicle may include an expandable curtain and/or an expandable bladder configured to be expanded from a stowed state to a deployed state. In the deployed state, the expandable curtain may include first and second sides configured to extend along respective portions of first and second interior sides of the vehicle. The expandable curtain may also include a transverse portion extending between the first and second sides, and the first and second sides, and the transverse portion of the expandable curtain may form a contiguous barrier. The expandable bladder when deployed may be supported by the expandable curtain during contact by an occupant. The occupant protection system may also include a deployment control system configured to cause the expandable curtain and/or the expandable bladder to expand to the deployed state. The occupant protection system may be used in vehicles having a carriage-style seating arrangement.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.

Abstract: Vehicles may be associated with a variety of different types of events, including events associated with vehicle operations and/or events associated with vehicle passengers. The present disclosure is related to, when an event is detected, exchanging information with a vehicle passenger, such as via the passenger's mobile device and/or wearable device. In some instances, an event may be detected, and examples of the present disclosure may provide an application notification presenting various information. The notification may, in some examples, be configured to help the passenger locate the passenger device, to alert the passenger to the event, to provide instructions, and/or to provide a control interface for controlling a vehicle operation. In some examples, the notification may supersede operations of the passenger device, such as by being presented even if the device is in a locked state or has an unrelated application open and in the foreground.