The present document relates to a simulation method for an autonomous vehicle, a method for controlling the autonomous vehicle, a device, an electronic apparatus, a computer-readable storage medium, and a computer program product. The method for the simulation of the autonomous vehicle comprises acquiring current state information of the autonomous vehicle; performing the simulation based on the current state information to acquire the prediction information of the autonomous vehicle; and sending the prediction information to the autonomous vehicle.

A vehicle and a system and method of controlling the vehicle. The system includes a sensor and a processor. The sensor obtains a first estimate of a force on a tire of the vehicle based on dynamics of the vehicle. The processor is configured to obtain a second estimate of the force on the tire using a tire model, determine an estimate of a coefficient of friction between the tire and the road from the first estimate of the force and the second estimate of the force, and control the vehicle using the estimate of the coefficient of friction.

In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

A vehicle and a system method of operating the vehicle is disclosed. The system includes a monitoring module and a mitigation module operating on a processor. The monitoring module is configured to measure a degradation in an operation parameter of the vehicle, the vehicle operating in a first state based on a first value of a set of adaptive parameters. The mitigation module is configured to determine a threat to the vehicle due to operating the vehicle in the first state with the degradation in the operation parameter and adjust the set of adaptive parameters from the first value to a second value that mitigates the threat to the vehicle, wherein the processor operates the vehicle in a second state based on the second value.

A method of switching a control right according to a driving mode of a vehicle in a control right switching system operated by at least one processor is provided. Upon receiving a command to switch a driving mode of a vehicle being in any one driving mode of a manual driving mode or an autonomous driving mode, a torque value of a control unit that controls the vehicle according to a current driving mode of the vehicle is initialized. Furthermore, after transferring a driving mode control right of the vehicle from a vehicle to a driver or from the driver to the vehicle by increasing or reducing a vehicle control right to control the vehicle, whether to switch the driving mode is determined through monitoring information obtained by the control unit in the vehicle whose driving mode was switched during a predetermined time period.

This disclosure describes systems and methods for training a neural network with a training data set including data items labeled at different granularities. During training, each item within the training data set can be fed through the neural network. For items with labels of a higher granularity, weights of the network can be adjusted based on a comparison between the output of the network and the label of the item. For items with labels of a lower granularity, an output of the network can be fed through a conversion function that convers the output from the higher granularity to the lower granularity. The weights of the network can then be adjusted based on a comparison between the converted output and the label of the item.

This disclosure describes systems and methods for training a neural network with a training data set including data items labeled at different granularities. During training, each item within the training data set can be fed through the neural network. For items with labels of a higher granularity, weights of the network can be adjusted based on a comparison between the output of the network and the label of the item. For items with labels of a lower granularity, an output of the network can be fed through a conversion function that convers the output from the higher granularity to the lower granularity. The weights of the network can then be adjusted based on a comparison between the converted output and the label of the item.

In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first detect a URU within a threshold distance of a drivable area that a vehicle is traversing or will traverse. The system will then receive perception information relating to the URU, and use a plurality of features associated with each of a plurality of entry points on a drivable area boundary that the URU can use to enter the drivable area to determine a likelihood that the URU will enter the drivable area from that entry point. The system will then generate a trajectory of the URU using the plurality of entry points and the corresponding likelihoods, and control navigation of the vehicle while traversing the drivable area to avoid collision with the URU.