Motional Ad LLC

Massachusetts Institute of Technology

Nutonomy Inc.

Nanyang Technological University

Emilio Frazzoli

Hsun-Hsien Chang

Baoxing Qin

Eric Wolff

Kostyantyn Slutskyy

Scott D Pendleton

Andrea Censi

Vu Anh Huynh

Harshavardhan Ravichandran

Tichakorn Wongpiromsarn

Elena Corina Grigore

Tung Minh Phan

Freddy Boulton

Karl D Iagnemma

Paul Schmitt

Yu Wang

Radboud Duintjer Tebbens

Francesco Seccamonte

Anne Collin

Calin Belta

Amitai Bin-Nun

Cristhian Guillermo Lizarazo Jimenez

Tawit Uthaicharoenpong

Luke Fletcher

Danwei Wang

Juraj Kabzan

Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Trajectory prediction from precomputed or dynamically generated bank of trajectories

Among other things, we describe techniques for adjusting lateral clearance for a vehicle using a multi-dimensional envelope. A trajectory is generated for the vehicle. A multi-dimensional envelope is generated indicating a drivable region for the vehicle and containing the trajectory. One or more objects are identified located along or adjacent to the trajectory. At least one dimension of the generated multi-dimensional envelope is adjusted to adjust a lateral clearance between the vehicle and the identified one or more objects. A control module of the vehicle navigates the vehicle along the multi-dimensional envelope.

Adjusting lateral clearance for a vehicle using a multi-dimensional envelope

Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

Communicating vehicle information to pedestrians

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, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.

Planning for unknown objects by an autonomous vehicle

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

Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

Mixed-mode driving of a vehicle having autonomous driving capabilities

Among other things, a model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the model.

A computer-based method controls a dynamical system in an uncertain environment within a bounded probability of failure. The dynamical system has a state space and a control space. The method includes diffusing a risk constraint corresponding to the bounded probability of failure into a martingale that represents a level of risk tolerance associated with the dynamical system over time. The state space and the control space of the dynamical system are augmented with the martingale to create an augmented model with an augmented state space and an augmented control space. The method may include iteratively constructing one or more Markov Decision Processes (MDPs), with each iterative MDP represents an incrementally refined model of the dynamical system. The method further includes computing a first solution based on the augmented model or, if additional time was available, based on one of the MDP iterations.

Autonomous control of dynamical systems

Growing community of inventors

Zurich, Switzerland

Emilio Frazzoli