Pony Ai Inc.

Jun Peng

Xiang Yu

Tiancheng Lou

Hao Song

Sinan Xiao

Kai Chen

Yiming Liu

Nengxiu Deng

Yubo Zhang

Tianyi Li

Zhuo Zhang

Jie Hou

Zuoteng Chen

Cheng Jin

Zhongnan Hu

Bo Xiao

Zixuan Zhou

Tao Qin

Jianan Wang

Zichao Qi

Yin Zhong

Mengda Yang

Zhenhao Pan

Xing Xing He

Haoying Fu

Zhou Zhang

Systems, methods, and non-transitory computer-readable media are provided for implementing a tracking camera system onboard an autonomous vehicle. Coordinate data of an object can be received. The tracking camera system actuates, based on the coordinate data, to a position such that the object is in view of the tracking camera system. Vehicle operation data of the autonomous vehicle can be received. The position of the tracking camera system can be adjusted, based on the vehicle operation data, such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion. A focus of the tracking camera system can be adjusted to bring the object in focus. The tracking camera system captures image data corresponding to the object.

Systems and methods for implementing a tracking camera system onboard an autonomous vehicle

A set of sensor information may include first sensor information generated based on a first sensor of a first vehicle and second sensor information generated based on a second sensor of a second vehicle. Individual sensor information may characterize positions of objects in an environment of individual sensors. Relevant sensor information for a vehicle may be determined based on the set of sensor information and a position of the vehicle. The relevant sensor information may characterize positions of objects in a maneuver environment of the vehicle. A desired navigation of the vehicle in the maneuver environment of the vehicle may be determined based on the relevant sensor information. An instruction may be provided to the vehicle based on the desired navigation of the vehicle. The instruction may characterize one or more maneuvers to be performed by the vehicle to execute the desired navigation.

Distributed sensing for vehicle navigation

A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

Generative adversarial network enriched driving simulation

A sensor enclosure comprises a cover and a structure. The structure can be encased by the cover. The structure comprises a frame, a ring, and one or more anchoring posts. The frame can be configured to mount one or more sensors. The ring, disposed peripherally to the frame, can be operatively coupled to the cover. The ring can include a drainage ring plate that drains rainwater accumulated on the cover away from the sensor enclosure. The one or more anchoring posts, disposed underneath the frame and the ring, can be used to anchor the sensor enclosure to a vehicle.

Sensor enclosure drainage

An adaptive filter system and a method for controlling the adaptive filter system are described herein. The system can includes one or more filters to attenuate incoming light. The one or more filters can be moved by one or more actuators. The method can capture image data from an imaging device through the one or more filters. Information can be determined from the captured image data. The one or more filters can be moved to a position for capturing image data based on the information.

Adaptive filter system for self-driving vehicle

A sensor enclosure comprising a domed cover and a base. The base can be encased by the domed cover. The base comprises an inner frame, an outer frame, one or more wipers, and a powertrain. The inner frame can provide surfaces for one or more sensors. The outer frame, disposed underneath the inner frame, the outer frame includes a slewing ring. The slewing ring comprises an inner ring to which the domed cover is attached and an outer ring attached to the outer frame. The one or more wipers extends vertically from the outer frame, each wiper having a first end attached to the outer frame and a second end attached to a support ring, and each wiper making a contact with the dome cover. The powertrain, disposed within the outer frame, configured to rotate the ring and the dome cover attached to the inner ring.

Alternative wiper mechanism to remove rainwater on a sensor enclosure

Systems, methods, and non-transitory computer readable media configured to generate enhanced training information. Training information may be obtained. The training information may characterize behaviors of moving objects. The training information may be determined based on observations of the behaviors of the moving objects. Behavior information may be obtained. The behavior information may characterize a behavior of a given object. Enhanced training information may be generated by inserting the behavior information into the training information.

Enhanced training information generation

Systems, methods, and non-transitory computer readable media may be configured to calibrate sensor measurements based on detection of brake light. Acceleration information of a first vehicle may be obtained. The acceleration information may define an acceleration probability distribution of the first vehicle. Image information may be obtained. The image information may define an image of the first vehicle. Whether a brake light of the first vehicle is on or off may be determined based on the image of the first vehicle. Based on a determination that the brake light of the first vehicle is on, a calibrated acceleration probability distribution of the first vehicle may be generated based on the acceleration probability distribution of the first vehicle and a braking-calibration curve.

Measurements incorporating brake light detection

Systems, methods, and non-transitory computer readable media may be configured to characterize optical characteristics of optical elements. An optical element mount may be configured to carry an optical element. A calibration display may be configured to display a calibration object. The calibration object may include a known visual pattern. Multiple images of the calibration object may be obtained. The multiple images may be acquired using the optical element carried by the optical element mount. The multiple images may include different perspectives of the calibration object. Optical characteristics of the optical element may be characterized based on the known visual pattern and the different perspectives of the calibration object.

Characterizing optical characteristics of optical elements

A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: detecting one or more movable traffic objects; determining one or more target movable traffic objects from the one or more detected movable traffic objects; determining a type of the one or more target movable traffic objects and an traffic object that has a right of way (ROW) in a situation involving the autonomous-driving vehicle. The system further performs: determining a manner of generating a vehicle behavior notification to the target movable traffic object based on the type of the one or more target movable traffic objects and the ROW; and causing a vehicle behavior notification of the determined manner to be generated to the one or more target movable traffic objects.

On-site notification from autonomous vehicle for traffic safety

Systems, methods, and non-transitory computer-readable media are provided for implementing a preemptive suspension control for an autonomous vehicle to improve ride quality. Data from one or more sensors onboard the autonomous vehicle can be acquired. A surface imperfection of a road can be identified from the data. A next action for the autonomous vehicle can be determined based on the road condition. A signal can be outputted that causes the autonomous vehicle to act in accordance with the next action after adjusting the suspension preemptively.

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Fremont, CA, United States of America

Jun Peng