Motional AD LLC

Various measurement systems and methods are disclosed to enable characterizing the optical characteristics of light beams emitted by a light detection and range finding (LIDAR) system or sensor and evaluating the range finding function of user selected lidar channels while the lidar operates under a real operational condition and is exposed to a range of user defined environmental conditions.

Provided are methods for processing raw data for sensors with multiple color channels, which can include receiving raw image data associated with an image sensor, wherein the raw image data includes a plurality of groups of pixels including a first group of pixels associated with a first filter and a second group of pixels associated with a second filter. The methods may also include generating, from the raw image data, a plurality of wavelength-based data groups including a first wavelength-based data group and a second wavelength-based data group. The first wavelength-based data group may include first pixels corresponding to the first group of pixels and the second wavelength-based data group may include second pixels corresponding to the second group of pixels. The methods may also include encoding the first wavelength-based data group and the second wavelength-based data group. Systems and computer program products are also provided.

A system may be used to determined object characteristics and/or generate bounding boxes for objects in a vehicle scene by enriching later-in-time feature maps using earlier-in-time feature maps. The system may generate a feature map from a received. Using an earlier-in-time feature map, the system may enrich semantic data of the generated feature map to form an enriched feature map. The system may use the enriched feature map to generate one or more object characteristics of an object in the scene.

Provided are methods for gridlock prevention, which can include obtaining sensor data, obtaining an intersection parameter, and determining a constraint. Some methods described also include generating trajectories and providing data associated with a selected trajectory, such as for operation of an autonomous vehicle along the trajectory. Systems and computer program products are also provided.

Provided are methods for systems and methods for autonomous driving based on human-driven data, which can include obtaining sensor data associated with an environment in which a vehicle operates, determining a set of candidate trajectories, determining a human-driven trajectory, generating a trajectory score for one or more candidate trajectories of the set of candidate trajectories, and causing an output to be provided to a device based on the trajectory score associated with the one or more candidate trajectories, wherein the output comprises one or more of: the human-driven trajectory, the one or more candidate trajectories, and the one or more trajectory scores. Systems and computer program products are also provided.

Provided are interchangeable ring lenses for sensing systems, which can include a LiDAR sensor configured to rotate about a rotation axis within an interchangeable ring lens and receive electromagnetic radiation. Some systems also include a mount portion of the interchangeable ring lens that includes a threaded connection for removably coupling the interchangeable ring lens to a base. Some systems also include the interchangeable ring lens being removable from the base by the threaded connection. Some systems also include an elastomeric seal for at least partially sealing the interchangeable ring lens to the base. Methods and computer program products are also provided.

Provided are methods, systems, and storage media for random traffic generation. Methods include determining parameters of a simulation including a volume, simulated agent types, and an simulated agent density. Initiating the simulation by a seed that identifies at least a starting location and a goal location of the simulation. Methods also include assigning goals to simulated agents within the volume, and executing the simulation wherein the volume is updated responsive to motion of the simulated vehicle.

Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.

A rule violation leading to a traffic conflict involving a vehicle and at least one agent in real-time driving scenarios, simulation, re-simulation or other application is determined to be the fault of the vehicle by determining whether the situation was “reasonably foreseeable,” determining whether the at least one agent is a vulnerable road user (VRU) and determining whether the at least one agent violated a higher priority rule than the rule violated by the vehicle. In an embodiment, on-vehicle decisions are based on a rulebook with a priority structure that accounts for responsibility and determines an “initiator” of the traffic conflict based on whether the vehicle or the at least one agent was the first to violate a rule and the first to violate a higher priority rule.

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.