Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes obtaining labels associated with various aspects of right-of-way interactions between an autonomous vehicle and an agent, wherein the right-of-way interactions occur when a human driver takes over for the autonomous vehicle and performs the right-of-way interactions, running an autonomous vehicle stack that is untrained for processing right-of-way interactions between the autonomous vehicle and the agent, injecting the labels into the autonomous vehicle stack and determining, based on the injecting of the labels into the autonomous vehicle stack, a performance of the autonomous vehicle stack.

Abstract: Systems and techniques are provided for school bus detection and response by a vehicle such as an autonomous vehicle. An example method can include receiving sensor data collected by one or more sensors of a vehicle while the vehicle is in a scene. The sensor data can provide information about a school bus in the scene. The example method can include, based on the sensor data, determining a status of one or more lights of the school bus to determine a state of the school bus; determining that the one or more lights indicate whether the school bus is loading or unloading passengers; determining a behavior of one or more vehicles in the scene with respect to the school bus based on one or more parameters; and determining a behavior of the vehicle with respect to the school bus and the one or more vehicles.

Abstract: Systems and techniques are provided for determining a directionality of temporary traffic lanes. An example method can include detecting, based on sensor data from sensors of a vehicle, a temporary traffic lane on a road configured for use by traffic to navigate the road in lieu of a pre-existing traffic lane on the road, wherein a boundary of the temporary traffic lane is defined by objects on the road; detecting, based on the sensor data, cues indicating a directionality of the temporary traffic lane based on a first indication of a direction of travel of a vehicle traveling through the temporary traffic lane or an adjacent temporary traffic lane, a second indication of directionality provided by a human traffic controller in the road, and/or a third indication of directionality predicted based on objects on the road; and detecting the directionality of the temporary traffic lane based on the cues.

Abstract: Approaches to utilizing contextual right-of-way decision making for autonomous vehicles are disclosed. An autonomous vehicle is operated in a road setting within an operating environment having other road users. The operation of the autonomous vehicle is based on safety constraints providing limits on operation of the autonomous vehicle. The presence of a selected other road user within the operating environment is detected. Potential trajectories for the autonomous vehicle within the operating environment are evaluated with respect to the other road user. The autonomous vehicle interacts with the other road user by generating vehicle control signals based on a machine learned model and within the one or more safety constraints. The machine learned model is based on a hierarchy of costs corresponding to characteristics of maneuvers by the autonomous vehicle.

Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes obtaining labels associated with various aspects of right-of-way interactions between an autonomous vehicle and an agent, wherein the right-of-way interactions occur when a human driver takes over for the autonomous vehicle and performs the right-of-way interactions, running an autonomous vehicle stack that is untrained for processing right-of-way interactions between the autonomous vehicle and the agent, injecting the labels into the autonomous vehicle stack and determining, based on the injecting of the labels into the autonomous vehicle stack, a performance of the autonomous vehicle stack.

Abstract: Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.

Abstract: Disclosed herein is technology for deriving a planned path for a vehicle that is operating in a geographic area. In an example embodiment, a computing system associated with the vehicle may function to (i) identify a set of path priors to use as a basis for deriving the planned path for the vehicle in the geographic area, (ii) sample path priors in the identified set of path priors and thereby producing a set of points that is representative of the identified set of path priors, (iii) fit a curve to the set of points produced by the sampling, and (iv) derive the planned path for the vehicle based on the fitted curve.

Abstract: Examples disclosed herein may involve (i) obtaining data for one or more data variables related to autonomous operation of a vehicle in a test environment being facilitated by the vehicle's autonomy system, (ii) based on the obtained data, evaluating one or more predefined fault rules, each of which comprises (a) one or more predefined criteria related to the one or more data variables and (b) a predefined fault that is to be injected into the autonomy system when the one or more predefined criteria are determined to be satisfied, (iii) based on the evaluation, injecting a predefined fault into the autonomy system, and (iv) capturing data indicative of a response by a response mechanism of the vehicle to the vehicle autonomously operating in accordance with the injected fault.

Abstract: Disclosed herein is technology for deriving a planned path for a vehicle that is operating in a geographic area. In an example embodiment, a computing system associated with the vehicle may function to (i) identify a set of path priors to use as a basis for deriving the planned path for the vehicle in the geographic area, (ii) sample path priors in the identified set of path priors and thereby producing a set of points that is representative of the identified set of path priors, (iii) fit a curve to the set of points produced by the sampling, and (iv) derive the planned path for the vehicle based on the fitted curve.

Abstract: Examples disclosed herein may involve (i) obtaining data for one or more data variables related to autonomous operation of a vehicle in a test environment being facilitated by the vehicle's autonomy system, (ii) based on the obtained data, evaluating one or more predefined fault rules, each of which comprises (a) one or more predefined criteria related to the one or more data variables and (b) a predefined fault that is to be injected into the autonomy system when the one or more predefined criteria are determined to be satisfied, (iii) based on the evaluation, injecting a predefined fault into the autonomy system, and (iv) capturing data indicative of a response by a response mechanism of the vehicle to the vehicle autonomously operating in accordance with the injected fault.