Lyft, Inc.

Woven Planet North America, Inc.

Woven by Toyota, Inc.

Woven by Toyota, U.s., Inc.

Michael Jared Benisch

Sameer Qureshi

Emilie Jeanne Anne Danna

Luc Vincent

John S Houston

Ashesh Jain

Alan Agon

Mason Lee

Daniel Walker

Examples disclosed herein may involve a computing system that is operable to (i) identify, within a given period of operation of a vehicle having an associated sensor system for capturing sensor data, one or more times when the vehicle was driving in a lane having substantially-straight lane geometry, (ii) for each identified time, determine a respective measure of a lateral offset between the vehicle's associated sensor system and a lateral reference point of the vehicle, and (iii) based on the respective measure of the lateral offset that is determined for each of the one or more identified times, determine the lateral offset between the vehicle's associated sensor system and the lateral reference point of the vehicle.

Detecting positioning of a sensor system associated with a vehicle

Systems and methods for assessing the performance of an automated autonomous driving evaluator are disclosed herein. One embodiment receives a driving log including at least one of raw vehicle sensor data and information derived from the raw vehicle sensor data correlated with a time index; receives ground-truth data associated with the driving log, wherein the ground-truth data includes at least one of human-initiated disengagements of an autonomous driving system and human-entered annotations indicating mistakes made by the autonomous driving system; analyzes the driving log using an automated autonomous driving evaluator to generate a report; automatically compares the report with the ground-truth data to generate a performance assessment for the automated autonomous driving evaluator; and provides the performance assessment to a development process in which the automated autonomous driving evaluator is modified based, at least in part, on the performance assessment to improve the performance of the automated autonomous driving evaluator.

Systems and methods for assessing the performance of an automated autonomous driving evaluator

In one embodiment, a method includes, by a computing system associated with a vehicle, determining a current location of the vehicle in a first region, identifying one or more first sets of model parameters associated with the first region and one or more second sets of model parameters associated with a second region, generating, using one or more machine-learning models based on the first sets of model parameters, one or more first inferences based on first sensor data captured by the vehicle, switching the configurations of the models from the first sets of model parameters to the second sets of model parameters, generating, using the models having configurations based on the second sets of model parameters, one or more second inferences based on second sensor data generated by the sensors of the vehicle in the second region, and causing the vehicle to perform one or more operations based on the second inferences.

Geolocalized models for perception, prediction, or planning

In particular embodiments, a computing system may determine a measured driving characteristic of a driving control system based on observations of vehicles driven by the driving control system. The system may determine a difference between the measured driving characteristic and a target driving characteristic, which is based on objectivations of one or more manually controlled vehicles. The system may determine an evaluation objective for the driving control system. The system may determine a weight function for the evaluation objective. The system may determine a score for the driving control system with respect to the evaluation objective by weighting the difference between the measured driving characteristic and the target driving characteristic using the weight function. The system may apply, based on the score, an adjustment to the driving control system to reduce a difference between a subsequently measured driving characteristic of the driving control system and the target driving characteristic.

Evaluating driving control systems for elegant driving

Systems, methods, and non-transitory computer-readable media can determine a raster representative of a surrounding environment of a vehicle, wherein the raster depicts one or more objects in the surrounding environment of the vehicle. A plurality of trajectory proposals are determined for a first object of the one or more objects. For each trajectory proposal of the plurality of trajectory proposals, a score indicative of a likelihood that the first object will take a trajectory consistent with the trajectory proposal, and an offset for modifying the trajectory proposal are generated. A predicted trajectory is determined for the first object based on the scores and the offsets for the plurality of trajectory proposals.

Systems and methods for automated trajectory prediction

In one embodiment, a method includes accessing a first set of driving metrics of a vehicle navigating a segment using a driving control system; computing, based on the first set of driving metrics, a driving characteristic for the vehicle. The driving characteristic of the vehicle being represented by the first set of driving metrics. The method also includes comparing the driving characteristic of the vehicle to a driving characteristic of a number of manually controlled vehicles represented by a second set of driving metrics collected while navigating the segment; and computing a score indicating an elegance of the driving control system in navigating the segment based on the comparison of the driving characteristic of the vehicle to the driving characteristic of the number of manually controlled vehicles.

Growing community of inventors

Menlo Park, CA, United States of America

Michael Jared Benisch