Abstract: Techniques for determining a modified lateral motion boundary of a vehicle in an environment are discussed herein. Techniques may include determining a lateral motion boundary for a vehicle in an environment, based partly on data associated with the environment and/or the vehicle, where representations of objects in the environment may be projected onto the lateral motion boundary. The lateral motion boundary may be modified based on such projections to determine a modified lateral motion boundary. The modified lateral motion boundary may include an open lateral boundary, configured by closing off portions of the lateral motion boundary corresponding to the projections, allowing a vehicle to move laterally to traverse the environment and avoid the objects.

Abstract: Techniques are discussed herein for executing log-based driving simulations to evaluate the performance and functionalities of vehicle control systems. A simulation system may execute a log-based driving simulation including playback agents whose behavior is based on the log data captured by a vehicle operating in an environment. The simulation system may determine interactions associated with the playback agents, and may convert the playback agents to smart agents during the driving simulation. During a driving simulation, playback agents that have been converted to smart agents may interact with additional playback agents, causing a cascading effect of additional conversions. Converting playback agents to smart agents may include initiating a planning component to control the smart agent, which may be based on determinations of a destination and/or driving attributes based on the playback agent.

Abstract: Techniques are discussed herein for executing log-based driving simulations to evaluate the performance and functionalities of vehicle control systems. A simulation system may execute a log-based driving simulation including playback agents whose behavior is based on the log data captured by a vehicle operating in an environment. The simulation system may determine interactions associated with the playback agents, and may convert the playback agents to smart agents during the driving simulation. During a driving simulation, playback agents that have been converted to smart agents may interact with additional playback agents, causing a cascading effect of additional conversions. Converting playback agents to smart agents may include initiating a planning component to control the smart agent, which may be based on determinations of a destination and/or driving attributes based on the playback agent.

Abstract: A method is provided comprising receiving log data associated with a vehicle traversing a real-world environment, and inputting the log data into a search algorithm. A first modified value of a first parameter associated with the log data is received from the search algorithm. The search algorithm determines the first modified value based on a first simulation score associated with occurrence of a first event in a first run of a driving simulation. The first run of the driving simulation includes an autonomous vehicle and is based at least in part on the first modified value of the first parameter. First modified log data based at least in part on the first modified value of the first parameter is output, and may be used for training a machine learning model such as a machine learning model for controlling an autonomous vehicle.

Abstract: Driving simulations may be generated based on driving log data and used to validate autonomous vehicle safety systems. A driving simulation system may receive driving log data from autonomous vehicles and determine events which caused a vehicle to diverge from a planned trajectory. Driving simulations may be generated based on the driving log data, and executed using a simulated vehicle that follows the initial planned trajectory of the autonomous vehicle. The driving simulations may be analyzed to detect potential collisions and near misses, and to determine success conditions for the behaviors of vehicle safety systems. Driving simulations and associated success conditions may be aggregated and used in validation suites for vehicle controllers and/or vehicle safety systems, thereby providing more comprehensive and robust autonomous vehicle validation based on real-world driving scenarios.

Abstract: Techniques are discussed herein for executing log-based driving simulations to evaluate the performance and functionalities of vehicle control systems. A simulation system may execute a log-based driving simulation including playback agents whose behavior is based on the log data captured by a vehicle operating in an environment. The simulation system may determine interactions associated with the playback agents, and may convert the playback agents to smart agents during the driving simulation. During a driving simulation, playback agents that have been converted to smart agents may interact with additional playback agents, causing a cascading effect of additional conversions. Converting playback agents to smart agents may include initiating a planning component to control the smart agent, which may be based on determinations of a destination and/or driving attributes based on the playback agent.

Abstract: Techniques are discussed herein for executing log-based driving simulations to evaluate the performance and functionalities of vehicle control systems. A simulation system may execute a log-based driving simulation including playback agents whose behavior is based on the log data captured by a vehicle operating in an environment. The simulation system may determine interactions associated with the playback agents, and may convert the playback agents to smart agents during the driving simulation. During a driving simulation, playback agents that have been converted to smart agents may interact with additional playback agents, causing a cascading effect of additional conversions. Converting playback agents to smart agents may include initiating a planning component to control the smart agent, which may be based on determinations of a destination and/or driving attributes based on the playback agent.

Abstract: A system for generating simulated LiDAR data may include a depth offset generator and an intensity value generator. The depth offset generator may be configured to receive environment data including depth information, e.g., a depth map, the environment. The depth offset generator may determine an optical flow field from the depth information and estimate depths for positions to simulate LiDAR sensor data. field. The depth offset generator can also generate timestamp information based on attributes of the simulated LiDAR sensor, and determine the estimated depths using the timestamp information. The intensity value generator may be configured to determine an intensity for pixels based on physical attributes associated with those pixels. The simulated LiDAR data may be used in simulations run on autonomous vehicle control systems.