Denso International America, Inc.

Robotwits LLC

Denso Corporation

Waymo LLC

Ellis Ratner

Takashi Bando

Yunfei Xu

Maxim Likhachev

Lu Lyu

Jonathan Michael Butzke

Akihito Iwai

Savio Joseph Pereira

Abhinav Srinivas Sai Kiran Kunapareddy

A multi-dimensional trajectory planning system is disclosed that includes planning and actuator modules. The planning module executes the planning application to: determine a first dimensionality including first dimensions for a first stage, where each of the first dimensions are active, and where the first dimensions include two or more dimensions; determine a second dimensionality including second dimensions for a second stage, where the second dimensions include the first dimensions or a subset of the first dimensions, and where the second stage has a lower level of dimensionality than the first stage; based on map data and sensor data, estimates first possible future states of the first dimensions for the first stage, and estimates second possible future states of the second dimensions for the second stage based on the first possible future states; and selects a trajectory plan based on the second possible future states.

Multi-dimensional extended trajectory planning with changing dimensionality based on horizon steps

Autonomous driving systems and methods include a controller of a first vehicle configured to detect an operating state of a second vehicle proximate the first vehicle, and to predict, based on the operating state of the second vehicle, a potential behavior for the second vehicle that optimizes a cost function from the perspective of the second vehicle. The controller then controls the first vehicle to avoid a collision with the second vehicle assuming the second vehicle operates according to the potential behavior.

Vehicle driving system

An autonomous drive system for a vehicle includes at least one environment perception sensor, a perception module, a location module, an intention prediction module, and a control module. The perception module is configured to receive signals from the at least one environment perception sensor and detect and track at least two remote vehicles in one or both of a current lane and a neighboring lane. The location module is configured to determine a location of the vehicle. The intention prediction module is configured to generate predicted trajectories for the at least two remote vehicles. The control module is configured to receive signals from the at least one environment perception sensor and receive the predicted trajectories from the intention prediction module. The control module determines a location and time for a lane change based on the predicted trajectories and controls the vehicle to change lanes at the determined location and time.

System for autonomous lane merging

A system and method are provided and include a subject vehicle having vehicle actuation systems. A driving context database stores driving trends associated with driving locations. A state estimation module determines a current location of the subject vehicle. A global route planner module determines a route to an inputted destination. An action primitive planning module retrieves a driving trend associated with the current location, selects a sequence of action primitives based on the driving trend associated with the current location and based on the determined route, and generates waypoints for the subject vehicle to travel to based on the sequence of action primitives, each waypoint including location coordinates and a direction. A trajectory planner module determines a trajectory for the subject vehicle based on the waypoints. A vehicle control module controls the vehicle actuation systems based on the determined trajectory.

Data-driven planning for automated driving

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Pittsburgh, PA, United States of America

Ellis Ratner