Abstract: Among other things, techniques are described for path-based trajectory prediction (e.g., for autonomous driving). The technique includes: obtaining information about one or more agents in a portion of an environment where a vehicle is operating, the information including data associated with locations of the one or more agents in the portion of the environment; for each of at least one agent of the one or more agents, determining a reference path for the agent based on a location of the agent in the environment, and predicting a future trajectory of the agent based on the reference path; and operating the vehicle based on one or more predicted future trajectories of the at least one agent of the one or more agents.

Abstract: An apparatus for providing location information includes a plurality of sensor units which are distributed throughout a confined area lacking a global positioning system (GPS) coverage and have respectively a plurality of FOVs that at least partially overlap with each other in an overlap area, each of the plurality of sensor units including a first sensor and a second sensor of a type that is different from a type of the first sensor; and a processor. The processor is configured to fuse sensing data provided by the first sensor and the second sensor that are respectively included in each of the plurality of sensor units, identify a location of an object existing in the overlap area based on the fused sensing data, and provide location information of the identified location to the object.

Abstract: A control system for controlling movement of a vehicle, is disclosed. The control system is configured to construct a graph having multiple nodes defining states of the vehicle. The multiple nodes comprise an initial node defining initial state and a target node defining target state. Each pair of nodes is connected by an edge defined by collision-free motion primitives where the nodes comprise motion cusps. Multiple nodes connected through edges form a first path. A first number of motion cusps in the first path is determined and the graph is expanded to add new nodes until a termination condition is met on determining that the first number of motion cusps is above a threshold. The expansion of the graph is subjected to a constraint associated with a total number of motion cusps. Further a second path is determined having lesser motion cusps than the first path.

Abstract: A global multi-vehicle decision making system is disclosed for providing real-time motion planning and coordination of one or multiple connected and automated and/or semi-automated vehicles (CAVs) in an interconnected traffic network that includes one or multiple non-controlled vehicles (NCVs), one or multiple conflict zones and one or multiple conflict-free road segments. The system includes a receiver configured to acquire infrastructure sensing signals, at least one memory configured to store map and programs, and at least one processor configured to perform steps of formulating a global mixed-integer programming (MIP) problem using the infrastructure sensing signals, computing a motion plan for each CAV and each NCV in the traffic network by solving the global MIP problem, computing an optimal sequence of entering/exiting times and a sequence of average velocities for each CAV and each NCV, and computing a velocity profile and/or one or multiple planned stops for each CAV.

Abstract: A driving system includes a vehicle computing device on a vehicle and a cloud computing device at a location physically separated from the vehicle. The vehicle computing device transmits navigation information to the cloud computing device, including destination information, location information of the vehicle, and high-definition map information regarding a surrounding environment of the vehicle. The cloud computing device generates, based on the navigation information, path information including primary path information and backup path information and transmits the path information to the vehicle computing device. The vehicle computing device controls a trajectory of the vehicle based on the received path information.

Abstract: A method, a system, and a non-transitory storage media for determining agent importance prediction for autonomous driving. Input features associated with agents present in an environment surrounding a vehicle are processed. Output features associated with the agents are determined based on the input features. An importance of each agent is predicted using the output features. One or more movements of the vehicle are determined based on the predicted agent importance.

Abstract: A system, device and method of managing autonomous vehicles are provided. The system may include a server, a feedback device, a control device for controlling a vehicle. A server may include a communication interface configured to communicate with a control device of each of a plurality of vehicles and a feedback device; a memory storing instructions; and at least one processor. The at least one processor is configured to receive control data from the control device of each of the plurality of vehicles; determine an operation status of each of the plurality of vehicles based on the control data; generate a feedback interface based on the operation status of each of the plurality of vehicles; and transmit the feedback interface to the feedback device.

Abstract: Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled.

Abstract: Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.

Abstract: Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.

Abstract: A driving system includes a vehicle computing device on a vehicle and a cloud computing device at a location physically separated from the vehicle. The vehicle computing device transmits navigation information to the cloud computing device, including destination information, location information of the vehicle, and high-definition map information regarding a surrounding environment of the vehicle. The cloud computing device generates, based on the navigation information, path information including primary path information and backup path information and transmits the path information to the vehicle computing device. The vehicle computing device controls a trajectory of the vehicle based on the received path information.

Abstract: An apparatus for providing location information includes a plurality of sensor units which are distributed throughout a confined area lacking a global positioning system (GPS) coverage and have respectively a plurality of FOVs that at least partially overlap with each other in an overlap area, each of the plurality of sensor units including a first sensor and a second sensor of a type that is different from a type of the first sensor; and a processor. The processor is configured to fuse sensing data provided by the first sensor and the second sensor that are respectively included in each of the plurality of sensor units, identify a location of an object existing in the overlap area based on the fused sensing data, and provide location information of the identified location to the object.

Abstract: A system, device and method of managing autonomous vehicles are provided. The system may include a server, a feedback device, a control device for controlling a vehicle. A server may include a communication interface configured to communicate with a control device of each of a plurality of vehicles and a feedback device; a memory storing instructions; and at least one processor. The at least one processor is configured to receive control data from the control device of each of the plurality of vehicles; determine an operation status of each of the plurality of vehicles based on the control data; generate a feedback interface based on the operation status of each of the plurality of vehicles; and transmit the feedback interface to the feedback device.

Abstract: Described herein is a lifting device for raising and lowering a contact member. The lifting device includes an actuator configured to move the contact member between a lowered position and a raised position, wherein in a first mode of operation of the lifting device, the actuator moves the contact member from the lowered position to the raised position without rotating the contact member about a reference axis, and in a second mode of operation of the lifting device, the actuator permits rotational motion of the contact member about the reference axis from the raised position to a modified raised position.

Abstract: Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.

Abstract: Described herein is a lifting device for raising and lowering a contact member. The lifting device includes an actuator configured to move the contact member between a lowered position and a raised position, wherein in a first mode of operation of the lifting device, the actuator moves the contact member from the lowered position to the raised position without rotating the contact member about a reference axis, and in a second mode of operation of the lifting device, the actuator permits rotational motion of the contact member about the reference axis from the raised position to a modified raised position.