Motional Ad LLC

Amitai Bin-Nun

Radboud Duintjer Tebbens

Anne Collin

Calin Belta

Hsun-Hsien Chang

Emilio Frazzoli

Tichakorn Wongpiromsarn

Paul Schmitt

Kostyantyn Slutskyy

Dmytro S Yershov

Cristhian Guillermo Lizarazo Jimenez

Eliseo Miranda

Noushin Mehdipour

Ji Hyun Jeong

Felice Ling

Helen Argiroff-Flood

Provided are methods for autonomous vehicle yielding, which can include obtaining sensor data associated with an environment and obtaining a rule indicative of a target expressive operation. Some methods described also include determining whether the sensor data meets a first criterion, applying the rule in response to the sensor data meeting the first criterion, evaluating a first trajectory of the autonomous vehicle, and selecting a second trajectory. Systems and computer program products are also provided.

Autonomous vehicle yielding

Enclosed are embodiments of motion control operations in various traffic scenarios in consideration of the kinematic factor for trajectory planning. In some embodiments, a method includes: determining a danger rating for at least one object identified in an environment, wherein the danger rating represents a perceived risk associated with a respective object; evaluating a set of hierarchical factors with respect to a traffic scenario, wherein a metric is derived for trajectories of the traffic scenario that quantifies passenger ride comfort based on the danger rating and the set of hierarchical factors; determining a motion control operation in the traffic scenario to increase the passenger ride comfort based on the metric; and augmenting a route planner of an autonomous vehicle with motion control operations in different traffic scenarios to increase the passenger ride comfort.

Ride comfort improvement in different traffic scenarios for autonomous vehicle

In an example method, a computer system receives first data representing a plurality of scenarios for estimating a performance of a vehicle in conducting autonomous operations. Further, the computer system determines, for each of the scenarios: (i) a first metric indicating an observed performance of the vehicle in that scenario, where the first metric is determined based on at least one rule, and (ii) a second metric indicating a degree of information gain associated with that scenario. The computer system selects a subset of the scenarios based on the first metrics and the second metrics, and outputs second data indicative of the subset of the scenarios.

Selecting testing scenarios for evaluating the performance of autonomous vehicles

Enclosed are embodiments for scenario-based behavior specification and validation. In an embodiment, a method comprises: obtaining, using at least one processor, at least one trajectory associated with a driving scenario for an autonomous vehicle system; evaluating, using the at least one processor and at least one rulebook, the at least one trajectories to determine whether the at least one trajectory violates at least one rule in the at least one rulebook, wherein each rule in the rulebook is associated with at least one violation metric that is used to determine a degree to which the rule was satisfied or violated; determining, using the at least one processor and the at least one violation metric, a score for the at least one trajectory; and sending, using the at least one processor, the score to at least one of a software module in a software stack of the autonomous vehicle system, a simulation of the autonomous vehicle system or as a report or in a visual presented through a user interface of a cloud-based platform.

Scenario-based behavior specification and validation

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Silver Spring, MD, United States of America

Amitai Bin-Nun