BACKGROUND

Autonomous vehicles, such as vehicles that do not require a human driver, can be used to aid in the transport of cargo or passengers from one location to another. Such vehicles may operate in a fully autonomous mode, or a partially autonomous mode where a person may provide some driving input. In order to operate in an autonomous mode, the vehicle may employ various on-board sensors to detect features of the external environment, and use received sensor information to perform various driving operations. However, the condition of the vehicle can impact the ability to operate effectively in a self-driving mode.

BRIEF SUMMARY

The technology relates to determining an anomalous condition of a vehicle operating in a self-driving mode, and taking corrective action based on the nature of the anomalous condition. For instance, an anomalous condition for a cargo vehicle may be a problem with the trailer structure, such as a flat tire, or a problem with the cargo itself due to weight distribution or other issues. As discussed below, aspects of the technology include evaluating an expected vehicle pose against a detected pose to identify a pose discrepancy, and determining the anomalous condition from information associated with the pose discrepancy.

According to one aspect of the technology, a vehicle is configured to operate in an autonomous driving mode. The vehicle comprises a driving system, a perception system, and a control system. The driving system includes a steering subsystem, an acceleration subsystem and a deceleration subsystem to control driving of the vehicle in the autonomous driving mode. The perception system includes a plurality of sensors, where each of the plurality of sensors is positioned along the vehicle (either externally or internally). The control system is operatively connected to the driving system and the perception system. The control system has one or more computer processors configured to: determine an expected pose of the vehicle based on a model of the vehicle; receive sensor data from the perception system; direct the driving system when operating in the autonomous driving mode based on the sensor data received from the perception system; compare the received sensor data against the expected pose of the vehicle; detect, based on the comparison between the received sensor data against the expected pose, a pose discrepancy; determine an anomalous condition of the vehicle based on the detected pose discrepancy; and effect a corrective action in response to the anomalous condition.

In one example, the anomalous condition is a flat tire, a component mechanical failure, a shift in cargo, or a loss of cargo. Here, the control system is configured to effect the corrective action in accordance with a type of the cargo.

In another example, the plurality of sensors includes (i) at least one of lidar, radar or a camera sensor, and the vehicle-specific information is pose information; or (ii) at least one of an acoustic sensor or a pressure sensor. In this case, the acoustic sensor is configured to detect audio information of one or more tires of the vehicle, and the pressure sensor is configured to detect either a tire pressure or a load distribution of cargo. The vehicle-specific information may include at least one of acoustical information from the acoustic sensor indicating a tire status or pressure information from the pressure sensor indicating at least one of instantaneous tire pressure or cargo load distribution.

In another example, the vehicle is a truck having a tractor unit, the tractor unit including a coupling system to adjustably couple to a trailer. According to one scenario, the vehicle further includes the trailer. According to another scenario, the detected pose discrepancy is a shift of the trailer from a predicted orientation relative to the tractor unit.

In yet another example, the model of the vehicle is a kinematic model stored in memory of the control system. In a further example, each of the plurality of sensors is configured to detect objects in an environment surrounding the vehicle. And in another example, each of the plurality of sensors is configured to detect vehicle-specific information about the vehicle.

According to another aspect of the technology, a method of operating a vehicle in an autonomous driving mode is provided. The method comprises determining, by one or more processors of the vehicle, an expected pose of the vehicle based on a model of the vehicle; obtaining, by one or more sensors of a perception system of the vehicle, sensor data during operation in the autonomous driving mode; receiving, by the one or more processors, the obtained sensor data from the perception system; directing, by the one or more processors, a driving system of the vehicle based on the received sensor data; determining, by the one or more processors, an actual pose of the vehicle according to the received sensor data; comparing, by the one or more processors, the actual pose with the expected pose; detecting by the one or more processors, based on the comparing actual and expected poses, a pose discrepancy; determining, by the one or more processors, an anomalous condition of the vehicle based on the detected pose discrepancy; and effecting a corrective action in response to the anomalous condition.

In one example, the anomalous condition is a flat tire, a component mechanical failure, a shift in cargo, or a loss of cargo. Here, the effecting the corrective action is either performed in accordance with a type of the cargo, or includes transmitting information regarding the anomalous condition to either another vehicle or to a remote assistance service.

In another example, the vehicle-specific information is pose information. In a further example, obtaining the sensor data includes at least one of detecting audio information of one or more tires of the vehicle or detecting either a tire pressure or a load distribution of cargo. In yet another example, the vehicle-specific information includes at least one of acoustical information indicating a tire status or pressure information indicating at least one of instantaneous tire pressure or cargo load distribution.

According to another example, detecting the pose discrepancy includes detecting a shift of a trailer of the vehicle from a predicted orientation relative to a tractor unit of the vehicle.

In a further example, the model of the vehicle is a kinematic model. In this case, the method may further comprise retrieving the kinematic model from an on-board storage system of the vehicle.

In another example, the sensor data indicates objects in an environment surrounding the vehicle. And in yet another example, the sensor data indicates vehicle-specific information about the vehicle.

