Abstract: Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

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 include a housing and extend from a portion of a body of a vehicle. The sensor pod housing may have energy absorbing structures configured to absorb and dissipate energy during an impact in order to protect a pedestrian.

Abstract: An occupant constraint system may include a belt marker on a seat belt in a passenger compartment of the vehicle for restraining a passenger during a collision. The occupant constraint system may include a camera system for producing marker position data that is indicative of a position of the marker, and a physical characteristic estimator that is uses the marker position data to produce a physical characteristic estimate of a physical characteristic estimate of the passenger. The occupant constraint system may include a passenger related system having a passenger function and the passenger related system may control the passenger function based at least in part on the physical characteristic estimate.

Abstract: Techniques for validating operation of a perception system that is configured to detect objects in an environment of a vehicle are described herein. The techniques may include receiving log data representing a real scenario in which the vehicle was traversing an environment. Based at least in part on the log data, an error may be identified that is associated with an output received from the perception system while the vehicle was traversing the environment. In some examples, a determination may be made that a magnitude of the error violates a perception system requirement, the requirement established based on a determination that the magnitude of the error would contribute to an adverse event in an alternative scenario. Based on the magnitude of the error contributing to the adverse event, data associated with the error may be output for use in updating the perception system to at least meet the requirement.

Abstract: A seat for a vehicle may include a seatback including a variable resistance support disposed within a volume of the seatback. The variable resistance support may further include one or more configurable arrays of resistive elements including collapsible structures and/or inflatable bladders that may be configured to provide energy dissipation and/or absorption during a collision event. One or more array subsets may be configured to be independently controllable and configured to provide adjustable support at a region of the seatback, the adjustable support corresponding to a mass of the occupant. Moreover, when at least a portion of the back of an occupant pushes against a front surface of the seatback due to the collision event, the arrays of resistive elements may be configured to compress and/or collapse, at least partially, to absorb an energy applied from an occupant's back associated with the collision event.

Abstract: Validating a component of an autonomous vehicle may comprise determining, via simulation, a likelihood that operation of the component will result in an adverse event. Such simulations may be based on log data developed from real world driving events to, for example, accurately model a likelihood that a scenario will occur during real-world driving. Because adverse events may be exceedingly rare, the techniques may include modifying a probability distribution associated the likelihood that a scenario is simulated, determining a metric associated with an adverse event (e.g., a likelihood that operating the vehicle or updating a component thereof will result in an adverse event), and applying a correction to the metric based on the modification to the probability distribution.

Abstract: An active suspension control system for a vehicle includes a mathematical model based on a modal expansion of the vehicle. Model parameters of the vehicle can be extracted from the modal expansion using sensor data generated on the vehicle, e.g., on demand and/or in real time. The model parameters and the modal expansion can be used to determine a vehicle state, predict future vehicle states, and control aspects of an active suspension system based on the predicted future vehicle states. The model parameters may also be used to update the mathematical model, e.g., to account for component wear over time, and/or to detect anomalies or defects in the active suspension system.

Abstract: Techniques for verification of interchangeable connectors are disclosed which include applying one or more identification tags to connectors. The identification tags have identifiers which may be associated with one another for connectors that correspond. The identifiers may be read from the identification tags when the connectors are connected to determine if the connected connectors are associated.

Abstract: Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. Steering angle constraints may be used to limit or prevent saturation of steering by only one set of wheels as well as to reduce sideslip. The steering angle constraints are vehicle independent and applicable across a fleet of different vehicles based on vehicle speed. For instance, the steering angle constraints establish angle limits that dynamically adjust based on vehicle speed to ensure vehicle velocity and acceleration remain within predefined limits as established by autonomous vehicle system design.

Abstract: A system for disinfecting an autonomous vehicle includes a light assembly having LEDs configured to emit UVC light into a passenger compartment of the autonomous vehicle. The light assembly includes a heat sink to dissipate heat generated by the LEDs. In examples, the heat sink is disposed in the path of forced air associated with a climate control system of the vehicle. The climate control system may be controlled based on a temperature proximate the LEDs and/or the heat sink. In some examples, the light assembly is integrated into the ceiling of the vehicle and can further include visible light emitters and/or other features.

Abstract: Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

Abstract: Techniques for receiving and processing sensor data captured by a fleet of vehicle are discussed herein. In some examples, a fleet dashcam system can receive sensor data captured by electronic devices on a fleet of vehicles and can use that data to detect collision and near-collision events. The data of the collision or near-collision event can be used to determine a simulation scenario and a response of an autonomous vehicle control to the simulation scenario and/or it can be used to create a collision heat map to aid in operation of an autonomous vehicle.

Abstract: Simulating output of a perception system may comprise receiving scenario data indicating a position associated with a simulated sensor and a position and/or identifier of an object, and instantiating a three-dimensional representation of an environment and the object (i.e., a simulated environment). The system may generate depth data indicating distances and/or positions of surfaces in the simulated environment relative to the simulated sensor position and determine a three-dimensional region of interest based at least in part on the depth data associated with at least a portion of the object. In some examples, the three-dimensional region of interest may be smaller than a size of the object, due to an occlusion by topology of the simulated environment and/or another object in the simulated environment.

Abstract: A vehicle can connect to multiple networks and can determine network parameters (e.g., available bandwidth, latency, signal strength, etc.) associated with the multiple networks. Additionally, the vehicle can access network map data associated with the multiple networks. As the vehicle traverses an environment, the vehicle can collect sensor data of the environment and/or vehicle data (e.g., vehicle pose, diagnostic data, etc.). Based on the network parameters and the network map data, the vehicle can optimize the use of the networks determine portions of the sensor data and/or vehicle data to transmit via the one or more of the multiple networks.

Abstract: Techniques are discussed herein for detecting and measuring faults within vehicle drive systems. In various implementations, actuator feedback monitors and/or vehicle performance monitors are used individually or in combination to determine faults within acceleration systems, braking systems, and/or steering systems of a vehicle. Different monitors may use different algorithms, models, and input data from the vehicle during operation, to compare a predicted drive system output to a corresponding measured output. Additionally, monitors may include associated sets of disable conditions used to distinguish drive system faults from other driving conditions. The outputs from actuator feedback monitors, vehicle performance monitors, and disable conditions may be analyzed to detect drive system faults and control the vehicle to address the faults and control the safe operation of the vehicle.

Abstract: Techniques for generating simulations to evaluate an update to a controller. The controller may be configured to control one or more functionalities of an autonomous and/or a semi-autonomous vehicle. A simulation computing system may receive a request to evaluate a first controller. The simulation computing system may generate a simulation based on data associated with a previous operation of the vehicle in an environment, the previous operation being controlled by a second controller (e.g., standard for evaluation, control version, etc.). The simulation computing device may cause the first controller to control a simulated vehicle in the simulation and may determine whether to validate the update to the controller based on a difference between first metrics associated with a control of the simulated vehicle by the first controller and second metrics associated with a control of the vehicle by the second controller.