Aspects of the disclosure provide for reducing inconvenience to other road users caused by stopped autonomous vehicles. As an example, a vehicle having an autonomous driving mode may be stopped at a first location. While the vehicle is stopped, sensor data is received from a perception system of the vehicle. The sensor data may identify a road user. Using the sensor data, a value indicative of a level of inconvenience to the road user caused by stopping the vehicle at the first location may be determined. The vehicle is controlled in the autonomous driving mode to cause the vehicle to move from the first location and in order to reduce the value.

A system for impact-based operation of an autonomous agent (equivalently referred to herein as an ego agent and autonomous vehicle) includes and/or interfaces with a computing subsystem (equivalently referred to herein as a computer and/or set of computers). A method for impact-based operation of an autonomous agent includes: receiving a set of inputs; predicting a set of future scenarios; and determining a set of metrics based on the set of future scenarios. Additionally or alternatively, the method can include operating the autonomous agent based on the set of metrics and/or any other processes.

A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

Systems and methods can improve passenger safety for an Autonomous Vehicle (AV) based on the integration of sensor data captured by the AV's interior and exterior sensors. The AV can determine passenger occupancy data corresponding to where each passenger is detected within the AV by the interior sensors. The AV can determine multiple sets of one or more driving actions that the AV can perform at a future time. The AV can generate crash impact data corresponding to where each passenger is detected from one or more simulated collisions between the AV and one or more objected detected by the exterior sensors when the AV performs one or more sets of driving actions from among the multiple sets. The AV can determine ranked sets of driving actions based on the passenger occupancy data and the crash impact data.

An information processing system according to an embodiment includes one or more hardware processors. The hardware processors acquire an n-dimensional vector. The hardware processors generate n coordinate arrays, where the n coordinate arrays is n pieces of n-dimensional arrays for which, with respect to each of elements of an m-th array (1≤m≤n), an element value having a same value as an index of an m-th dimensional coordinate of the elements is set. The hardware processors obtain n first probability distribution arrays including an output value of a probability density function as an element value corresponding to each of the n coordinate arrays, multiply n element values for each of elements corresponding to each of the n first probability distribution arrays, and obtain a second probability distribution array having a result of multiplication as an element value.

The method can include: receiving a set of inputs; determining a set of policies based on the set of inputs; determining a set of scores associated with the set of environmental policies; and evaluating the set of policies. Additionally or alternatively, the method can include operating the ego agent according to a selected policy and/or any other processes. The method functions to facilitate scoring of policies based on ‘feasibility’ for agents in an environment. Additionally or alternatively, the method can function to facilitate autonomous operation of a vehicle (e.g., based on policy-feasibility of agents in the environment). Additionally or alternatively, the method can function to facilitate intention estimation for agents in an environment.

Techniques for analysis of autonomous vehicle operations are described. As an example, a method of autonomous vehicle operation includes storing sensor data from one or more sensors located on the autonomous vehicle into a storage medium, performing, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle, generating, based on the simulated execution of the one or more programs and as part of a simulation, one or more control signal values that control a simulated driving behavior of the autonomous vehicle, and providing a visual feedback of the simulated driving behavior of the autonomous vehicle on a simulated road.

Systems and methods for testing a hands-off detection algorithm. The method includes determining a plurality of system behavior test conditions for the algorithm and selecting an orthogonal array defining a plurality of test cases based on the plurality of system behavior test conditions. The method includes generating, for each of the test cases, an expected test outcome. The method includes for each of the test cases, conducting a test of with the vehicle based on the orthogonal array to generate a plurality of actual test outcomes and generating a response table based on the test outcomes, including a plurality of system behavior test condition interactions. The method includes determining, for each of the interactions, a result rating based on the expected test outcomes and the actual test outcomes and identifying, within the response table, which one or more of the test conditions exhibits a high failure condition.

An automated driving system and method of autonomously driving a vehicle. The system includes for a vehicle including at least one sensor device configured to detect the vehicle position and sense environment characteristics of the vehicle, an electronic control device configured to control autonomous driving of the vehicle based on an output of the sensor device, in which the controlling of autonomous driving includes an autonomous overtaking functionality for overtaking by changing the lane, and disable the autonomous overtaking functionality, in case at least one of a set of predetermined overtaking conditions is not satisfied.

A method of setting a target longitudinal acceleration of a vehicle travelling along a road behind a leading vehicle, comprising: determining a lateral position of the leading vehicle; defining a lateral range extending from the leading vehicle and having a first subrange, a second subrange and a central subrange therebetween, wherein the lateral range increases with lateral distance of the leading vehicle from a centre of a lane in which the leading vehicle is located when the leading vehicle is changing lanes; determining a longitudinal range extending behind the leading vehicle; and setting the target longitudinal acceleration, for any longitudinal position of the host vehicle within the longitudinal range, to: a first value if a lateral position of the host vehicle is within the central subrange; and a second value greater than the first value if the lateral position is within the first or second subrange.