Plusai Limited

Siva Bhargav Ravella

Amit Kumar

Xiaoyu Huang

Qiqiang Hou

Robert Joseph Dingli

Anurag Ganguli

Chaozhe He

Lichun Yang

Yufeng Zhang

Muhammad Waqas

This application is directed to a centralized system for predicting vehicle trajectory. A computer system associated with a fixed installation having one or more first sensors obtains, from the first sensors, information of a plurality of vehicles that are traveling within a zone of interest of a road. The computer system generates a first travel trajectory for a first vehicle of the plurality of vehicles based on the information obtained from the one or more first sensors. The computer system sends, to the first vehicle, the first travel trajectory such that the first vehicle is configured to at least partially autonomously drive the first vehicle based on the first travel trajectory of the first vehicle.

Centralized prediction and planning for improving lane platooning and intersection vehicle behavior

This application is directed to improving vehicle steering control of an autonomous vehicle. A first vehicle includes a first sensor, one or more processors, and memory. The first vehicle acquires, from at least the first sensor, first data of one or more first objects in a vicinity of the first vehicle. The first vehicle receives from a computer system associated with a fixed installation having at least a second sensor, via a wireless network, second data corresponding to one or more second objects detected by at least the second sensor. The first vehicle adjusts, by the one or more processors, a steering behavior of the first vehicle according to the first data and the second data. The first vehicle at least partially autonomously drives the vehicle in a first trajectory along the first lane of the road according to the steering behavior of the first vehicle.

Steering lateral precision control combining infrastructure and vehicle intelligence

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

Techniques are described for providing a hands-off steering wheel detection warning. An example method can include a vehicle computer determining a real-time level of fatigue of a driver of an autonomous vehicle. The vehicle computer can determine an operating parameter associated with an environment in which the autonomous vehicle is traveling. The vehicle computer can generate, using a machine learning model, a predicted driving pattern of a second vehicle traveling in the environment. The vehicle computer can determine a time interval for providing a hands-off steering wheel detection warning based at least in part on the real-time level of fatigue of the driver, the operating parameter, and the predicted driving pattern of the second vehicle. The vehicle computer can identify a final time interval for providing a hands-off steering wheel detection warning. The vehicle computer can output the hands-off steering wheel detection warning after the final time interval has elapsed.

Variable safe steering hands-off time and warning

Techniques are described for determining failover to a redundant braking system. An example method can include a system determining a fault occurring in a braking system of an autonomous vehicle (AV) operating in an autonomous mode, the braking system comprising a first braking system and a second braking system, the fault occurring in at least one of the first braking system and the second braking system, and the AV using the first braking system. The system can further determine whether to switch from using the first braking system based at least in part on determining the fault. The system can further transmit to an alert system of the AV, a request to generate an alert for manual control of the AV based at least in part on determining the fault. The system can further deactivate the autonomous mode of the AV based at least in part on determining the fault.

Redundant brake and brake failover for autonomous vehicles

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 are configured to perform operations comprising obtaining, by a computing system, a lane-keeping safe set associated with constraints on lateral movement of a vehicle traveling on a road, generating, by the computing system, a lane-keeping safe set with preview based on the lane-keeping safe set and preview data, the preview data associated with characteristics of the road ahead of the vehicle, receiving, by the computing system, a steer command to control the lateral movement of the vehicle, and generating, by the computing system, a safe steer command by modifying the steer command based on the lane-keeping safe set with preview.

Safety filter with preview data to improve the safety of steer commands

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

Techniques are described for providing a hands-off steering wheel detection warning. An example method can include a vehicle computer determining a real-time level of fatigue of a driver of an autonomous vehicle. The method can further include the vehicle computer determining an operating parameter associated with an environment in which the autonomous vehicle is traveling. The method can further include the vehicle computer determining a time interval for providing a hands-off steering wheel detection warning based at least in part on the real-time level of fatigue of the driver, and the operating parameter. The method can further include the vehicle computer identifying a final time interval for providing a hands-off steering wheel detection warning. The method can further include the vehicle computer outputting the hands-off steering wheel detection warning after the final time interval has elapsed.

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

In one or more embodiments, a method comprises receiving, at a processor, sensor data from a sensor at a vehicle that is moving while in an autonomous mode. A position and an orientation of the vehicle based on the sensor data is determined at the processor. A lateral deviation of the vehicle from a planned path based on the position and the orientation of the vehicle while the vehicle is moving in the autonomous mode is calculated at the processor. In response to the lateral deviation exceeding a predefined lateral deviation threshold, the autonomous mode is disengaged.

Methods and apparatus for disengaging an autonomous mode based on lateral error of an autonomous vehicle

A method includes receiving, iteratively over time, sets of data including vehicle dynamics data, image data, sound data, third-party data, and wind speed sensor data, each detected at an autonomous vehicle and associated with a time period. The method also includes estimating a first wind speed and a first wind direction for each time period, in response to receiving the sets of data and based on the sets of data, via a processor of the autonomous vehicle. The method also includes iteratively modifying a lateral control and/or a longitudinal control of the autonomous vehicle based on the estimated first wind speed and the estimated first wind direction, via the processor of the autonomous vehicle and during operation of the autonomous vehicle.

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Santa Clara, CA, United States of America

Siva Bhargav Ravella