Plusai Limited

Nio Technology (anhui) Co., Ltd.

Xiaoyu Huang

Siva Bhargav Ravella

Chaozhe He

Amit Kumar

Hyoungju Seo

Mohammadhussein Rafieisakhaei

Robert Joseph Dingli

Aaron R Bailey

Lichun Yang

Bruno M Barthelemy

This application is directed to collecting traffic flow data to facilitate vehicle motion planning. A computer system collects, via a plurality of sensors that are positioned on a fixed installation at a road, data of vehicles that are traveling along a road. The computer system determines, according to the data, that the vehicles traveling along the road includes a first vehicle and a second vehicle ahead of the first vehicle, where the second vehicle is beyond a sensing range of the first vehicle. The computer system identifies, from the data, second vehicle data corresponding to the second vehicle, including a location and speed of the second vehicle. The computer system transmits the second vehicle data to the first vehicle such that the first vehicle is enabled to at least partially autonomously drive according to the second vehicle data and additional data collected by first sensors of the first vehicle.

Motion planning for autonomous vehicle driving using vehicle-to-infrastructure communication

This application is directed to collecting event-based vehicle traffic data to facilitate driving a vehicle. A computer system includes sensors that are positioned on a fixed installation at a road, one or more processors, and memory. The computer system monitors, using the plurality of sensors on the fixed installation, vehicle traffic data (e.g., associated with one or more events) in a zone of interest of the road over a period of time to generate historical traffic data. The computer system uses the historical traffic data to train a driving model of an at least partially autonomous vehicle. The computer system sends the driving model to one or more vehicles. The driving model is configured to be used by the one or more vehicles to at least partially autonomously drive in a first trajectory while the one or more vehicles are traveling through a similar zone of interest.

Automatic event capturing for autonomous vehicle driving

Sensor data is received at a processor of a vehicle including at least one sensor, a plurality of axles, and a plurality of tires coupled to the plurality of axles. A determination is made by the processor if at least one tire from the plurality of tires is faulty based on the sensor data. A determination is made by the processor of at least one side of the vehicle and at least one axle from the set of axles associated with the at least one tire in response to the determining that the at least one tire is faulty. A determination is made by the processor of at least one remedial action to be performed by the vehicle based on the sensor data, the at least one side, and the at least one axle.

Controlling a vehicle based on data processing for a faulty tire

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining parameters for an environment based on a regenerable elevation index; generating a speed profile for the environment based on the parameters; and generating control actions for control of a vehicle in the environment based on the speed profile.

Adaptive and fuel efficient planning and control

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining first speeds of first objects in a first lane and second speeds of second objects in a second lane; determining a first speed of traffic for the first lane based on the first speeds and a second speed of traffic for the second lane based on the second speeds; and generating a speed limit for a third lane based on the first speed of traffic and the second speed of traffic.

Surrounding traffic speed management system

A method includes an initial trailer health assessment and real-time trailer health monitoring. The initial trailer health assessment includes autonomous pre-trip maneuvers of the autonomous vehicle during a first time period, and detecting a pre-trip vehicle health condition. A vehicle health score is calculated based on the pre-trip vehicle health condition. If the vehicle health score is at least a threshold value, real-time trailer health monitoring is performed during a trip of the autonomous vehicle during a second time period, by actively monitoring vehicle dynamics data and/or image data associated with the autonomous vehicle, to determine a fault condition of the autonomous vehicle. If the fault condition meets a first criteria, a control parameter and/or a travel plan of the autonomous vehicle is adjusted. If the fault condition meets a second criteria different from the first criteria, a signal is sent to cause the autonomous vehicle to cease movement.

Methods and apparatus for compensating for unique trailer of tractor trailer with autonomous vehicle system

Methods, systems, and non-transitory computer-readable media estimate coasting behavior of a vehicle; identify a scenario associated with coasting in an environment of the vehicle; and based in part on the coasting behavior, generate a trajectory associated with coasting for the vehicle to move in the scenario.

Scenario-based motion planning and control for coasting

A representation of a first set of weights associated with a vehicle prior to a drive are received, the vehicle including a plurality of axles, a plurality of wheels attached to the plurality of axles, and a load. A warning is caused to be output in response to at least one weight from the first set of weights being outside a predetermined range. Longitudinal and lateral dynamics associated with the vehicle during the drive are determined. A second set of weights associated with at least one axle from the plurality of axles during the drive are determined. A determination is made if at least one remedial action should be performed based on the longitudinal dynamics, the lateral dynamics, and the second set of weights. In response to determining that the at least one remedial action should be performed, the at least one remedial action is caused to be performed.

Controlling a vehicle based on data processing of weights at axles of the vehicle

Methods, systems, and non-transitory computer readable media configured to perform operations comprise determining, by a computing system, speeds and stations of a plurality of leading vehicles traveling ahead of an ego vehicle; calculating, by the computing system, a coasting viability factor based on the speeds and stations of the plurality of leading vehicles and a speed and station of the ego vehicle; comparing, by the computing system, the coasting viability factor to a coasting viability threshold; and determining, by the computing system, whether the ego vehicle should coast based on the comparing.

Active coasting in traffic

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

Methods and apparatus for automated speed selection and retarder application in downhill driving of an autonomous tractor trailer

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising: determining whether a first type of condition associated with an input is satisfied; based at least in part on satisfaction of the first type of condition, generating an estimated value of a parameter associated with operation of a vehicle; and controlling the vehicle based at least in part on the estimated value of the parameter.

Real time estimation of critical vehicle parameters in autonomous systems

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a lead obstacle in front of an ego vehicle; determining an optimizable function associated with an optimized preferred following distance between the ego vehicle and the lead obstacle; and generating the optimized preferred following distance based on the optimizable function.

Adaptive automatic preventative braking (APB) distance

An electric vehicle braking system including a braking controller, a front braking system, and a rear braking system. The front braking system includes a front friction brake and a front regenerative braking system. The rear braking system includes a rear regenerative braking system and excludes a friction brake. The braking controller is configured to detect the front regenerative braking has reached a maximum force, detect additional deceleration is required, and, in response to detecting the front regenerative braking has reached the maximum force and detecting additional deceleration is required, apply the front friction brake.

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San Jose, CA, United States of America

Xiaoyu Huang