Motional Ad LLC

Scott D Pendleton

Hsun-Hsien Chang

Tichakorn Wongpiromsarn

Kostyantyn Slutskyy

Andrea Censi

Emilio Frazzoli

Shih-Yuan Liu

Oscar Olof Beijbom

Sourabh Vora

Yu Pan

You Hong Eng

Giancarlo Baldan

James Guo Ming Fu

Daniele De Francesco

Bence Cserna

Aravindkumar Vijayalingam

Yifan Yang

Hans Andersen

Sammy Omari

Guchan Ozbilgin

Jonathan L Wieskamp

Maurilio Di Cicco

Juraj Kabzan

Lubing Zhou

Kevin C Gall

Jiong Yang

Zhiliang Chen

Grigoriy Dubrovskiy

Theodore Alden

Titus Chua

Shu-Kai Lin

Puneet Singhal

Xiaojun Sun

Laith Sahawneh

Sachit Kaul

Ryan Trumpore

Priyanka Ashok

Katherine Colwell

Bing Han

Meshari Alhemaidi

Nicholas Merritt

Sai Sonti

Manuel Weiss

Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle, is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment where an autonomous vehicle is operating. The method includes determining, by the at least one processor, a set of agents in the environment based on the semantic image data. The method includes determining a set of predicted actions for at least one agent of the set of agents. The method includes determining, from the set of predicted actions, a set of secondary predicted actions for the at least one primary agent using semantic data. The method includes determining, from the set of predicted actions, a set of primary predicted actions other than secondary predicted actions The method includes generating a path for the autonomous vehicle based on the set of primary predicted actions.

Path generation based on predicted actions

Provided are methods for turn signal assignment in complex maneuvers, which can include receiving position information associated with a position of a vehicle. Some methods described also include determining, hierarchically and based at least on the position information, whether to activate a turn signal of a vehicle prior to activation of the turn signal based on a turn of the vehicle at a roadway intersection. Some methods described also include determining a time to activate the turn signal prior to the turn of the vehicle at the roadway intersection. Some methods described also include transmitting a control signal to activate the turn signal at the determined time. Systems and computer program products are also provided.

Turn signal assignment for complex maneuvers

Techniques are provided for operation of a vehicle using multiple motion constraints. The techniques include identifying an object using one or more processors of a vehicle. The vehicle has a likelihood of collision with the object that is greater than a threshold. The processors generate multiple motion constraints for operating the vehicle. At least one motion constraint includes a minimum speed of the vehicle greater than zero to avoid a collision of the vehicle with the object. The processors identify one or more motion constraints for operating the vehicle to avoid a collision of the vehicle with the object. The processors operate the vehicle in accordance with the identified motion constraints.

Operation of a vehicle using multiple motion constraints

Among other things, planning a motion of a machine having moving capabilities is based on strategic guidelines derived from various basic principles, such as laws, ethics, preferences, driving experiences, and road environments.

Planning autonomous motion

Among other things, techniques are described for implementing occlusion representations over road features. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include: obtaining area information of at least one area of interest for a vehicle, identifying occlusion data associated with the at least one area of interest, determining occlusion information associated with the at least one area of interest based on the area information and the occlusion data, providing the occlusion information for planning a route for the vehicle, and operating the vehicle in accordance with the planned route. The occlusion data includes data associated with the occlusion information, and the occlusion information has a smaller data size than the occlusion data.

Systems and methods for implementing occlusion representations over road features

The subject matter described in this specification is directed to a system and techniques for operating an autonomous vehicle (AV) at a multi-way stop intersection. After detecting the AV is at a primary stopline of the multi-way stop intersection, a planned travel path though the multi-way stop intersection is obtained. If the planned travel path of the AV through the multi-way stop intersection satisfies a set of one or more clearance criteria, the AV proceeds past the primary stopline. The clearance criteria include a criterion that is satisfied in response to detecting the AV is clear to safely merge into a travel lane corresponding to the planned travel path.

Navigating multi-way stop intersections with an autonomous vehicle

This disclosure describes an autonomous vehicle configured to obtain sensor data associated with objects proximate a projected route of the autonomous vehicle, determine static constraints that limit a trajectory of the autonomous vehicle along the projected route based on non-temporal risks associated with a first subset of the f objects, predict a position and speed of the autonomous vehicle as a function of time along the projected route based on the static constraints, identify temporal risks associated with a second subset of the objects based on the predicted position and speed of the autonomous vehicle, determine dynamic constraints that further limit the trajectory of the autonomous vehicle along the projected route to help the autonomous vehicle avoid the temporal risks associated with the second subset of the objects, and adjust the trajectory of the autonomous vehicle in accordance with the static constraints and the dynamic constraints.

Planning with dynamic state a trajectory of an autonomous vehicle

Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Operation of a vehicle using motion planning with machine learning

Among other things, techniques are described for determining precedence order at a multiway stop. In embodiments, identifications are assigned to tracks, and young tracks are compared to stale tracks. A young track matches a stale track based on one or more factors. An identification of the young track is reassigned to an identification of the stale track, wherein the young track is determined to match the stale track based on the one or more factors. An earliest time of appearance of agents is determined based on identifications and in view of perception obscured areas. A precedence order for navigating through the intersection is determined based on local rules, the identifications, and the earliest time of appearance of agents, and the vehicle proceeds through the multiway stop intersection in accordance with the precedence order.

Precedence determination at multi-way stops

Among other things, we describe techniques for operation of a vehicle based on measured load characteristics and/or passenger comfort. One or more sensors of the vehicle can measure passenger data and/or load data of the vehicle. The passenger data and/or load data of the vehicle can be used by the vehicle to determine how to navigate within the surrounding environment.

Systems and methods for controlling actuators based on load characteristics and passenger comfort

Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

Surface guided vehicle behavior

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. In some embodiments, a method comprises: determining at least one current or future constraint for a trajectory of a vehicle in an environment that is associated with the vehicle becoming immobile for an extended period of time; determining a stopping-reason for immobility of the vehicle based on determining the at least one current or future constraint for the trajectory of the vehicle in the environment; identifying a timeout threshold based on the stopping-reason, wherein the timeout threshold is an amount of time a planning system of the vehicle will wait before initiating at least one remedial action to address the immobility; identifying that the timeout threshold is satisfied; and initiating the at least one remedial action for the vehicle based on the identifying that the timeout threshold is satisfied.

Immobility detection within situational context

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Operational envelope detection with situational assessment

Growing community of inventors

Singapore, Singapore

Scott D Pendleton