The disclosed technology provides solutions for improving passenger drop-off functions implemented by an autonomous vehicle (AV). In some implementations, a process of the disclosed technology can include steps for collecting environmental data about an environment around an autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle, processing the environmental data to generate an area grid comprising a plurality of grid sections, and associating, based on the environmental data, one or more features with each of the plurality of grid sections. Systems and machine-readable media are also provided.

The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may a first output from a first sensor and a second output from a second sensor; identify a target object in the first output; and determine, based on the first output, a detected driving condition associated with the target object and whether the condition triggers a navigational constraint. If the navigational constraint is triggered, the system may cause a first navigational adjustment. If the navigational constraint is not triggered, the system may determine whether a representation of the target object is included in the second output. If the representation of the target object is included in the second output, the system may cause a second navigational adjustment. If the representation of the target object is not included in the second output, the system may forego any navigational adjustments.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

A vehicle control method includes recognizing a vicinity of a vehicle, setting a risk index for a traffic participant, and controlling a vehicle-mounted instrument of the vehicle based on the risk index which is set by the setter, and setting a risk index for a position at which the traffic participant will be present in the future based on ease of entry of the traffic participant from a sidewalk to a roadway adjacent to the sidewalk in a region that the traffic participant traveling on the sidewalk will enter in the future, and increasing a risk index to be set on the roadway side as there is a greater tendency for the traffic participant to enter the roadway.

Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle in the environment of the host vehicle; determine a next-state lateral distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a lateral braking distance for the host vehicle and the target vehicle based on a maximum yaw rate capability, a maximum change in turn radius capability, and a current lateral speed of the host vehicle and the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the lateral braking distances for the host vehicle and the target vehicle.

Methods and systems for multi-hypothesis object tracking for automated driving systems. One system includes an electronic processor configured to receive environment information and generate pseudo-measurement data associated with an object within an environment of the vehicle. The electronic processor is also configured to determine, based on the environment information and the pseudo-measurement data, a set of association hypotheses regarding the object. The electronic processor is also configured to determine, based on the set of association hypotheses, an object state of the object. The electronic processor is also configured to control the vehicle based on the determined object state.

Systems and methods are provided for navigating a host vehicle. In one implementation, a system may include a processing device configured to receive an image acquired by an image capture device; determine a planned navigational action for accomplishing a navigational goal of the host vehicle; analyze the at least one image to identify a first target vehicle ahead of the host vehicle and a second target vehicle ahead of the first target vehicle; determine a next-state distance between the host vehicle and the second target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a maximum braking capability of the host vehicle and a current speed of the host vehicle; and cause the vehicle to implement the planned navigational action if the stopping distance is less than the determined next-state distance.

Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

By a vehicle control notification device or a vehicle control notification method, an automated driving-related state of a vehicle is identified, a vehicle exterior notification device is controlled for performing vehicle exterior notification that is notification of information related to the automated driving toward an outside of the vehicle, and a type of the vehicle exterior notification is changed according to the identified automated driving-related state.

A method for conditional operation of an autonomous agent includes: collecting a set of inputs; processing the set of inputs; determining a set of policies for the agent; evaluating the set of policies; and operating the ego agent. A system for conditional operation of an autonomous agent includes a set of computing subsystems (equivalently referred to herein as a set of computers) and/or processing subsystems (equivalently referred to herein as a set of processors), which function to implement any or all of the processes of the method.