Tongji University

Wangsu Science & Technology Co., Ltd.

Signify Holding B.v.

Koninklijke Philips Corporation N.v.

Milliken + Co

Philips Lighting Holding B.v.

Conopco, Inc.

University of Delaware

Saic Motor Corporation Limited

Unilever IP Holdings B.v.

Unilever Global IP Limited

Hong Chen

Lin Zhang

Qiang Meng

Rongjie Yu

Hongxin Chen

Jinlong Hong

Hanghang Liu

Bin Li

Chunping Xie

Zhaolin Zhou

Wei Pan

Xiaodong Zhao

Shuang Zou

Shibiao Lv

Yanjun Huang

Haobo Sun

Jian Ding

Jian Wang

Jun Jiang

Xin Li

Abraham Rudolf Balkenende

Hailong Ge

Haitao Jiang

Xiaochuan Zhuang

Sidong Luo

Amitava Pramanik

Weizheng Zhou

Zidao Fan

Zhongwang Huang

Yong Sun

Zhenxing Fu

Zhixin Chen

Weimin Gao

Zhihua Li

Meihui Lu

Zhiwei Guo

Yunhui Li

Shuo Yang

Yanglong Chen

Patricia A DeLeon

Hong Zhang

Genevieve S Griffiths

Mingxun Zhang

Shuyou Yang

Haoqi Hu

Hansheng Lian

Zhengzhong Shao

Shibiao L V

Jinrong Yao

Han Cao

Bixia Liu

Kejia Zhu

Jiaxing Lu

Chunlai Zhao

Jiatong Du

Xincheng Li

Yulong Bai

Mengge Sun

Hangfeng Ji

Lulu Guo

Xiaoqiu Wang

An intelligent testing method for autonomous driving based on interaction of multiple background traffic participants, which includes the following steps: obtaining a test scenario and path information of traffic interactors in the test scenario, wherein the traffic interactors include a vehicle under test and background traffic participants, and the background traffic participants include background vehicles and background pedestrians; constructing a kinematic model of the background traffic participants based on the path information; dynamically constructing safety constraints between any two traffic interactors based on the path information; controlling the background traffic participants to conflict with the vehicle under test based on the kinematic model and the safety constraint, and constructing model prediction control to obtain a reference speed of the background traffic participants under collaborative interaction; and collecting real driving data of the vehicle under test and the background traffic participants in real time.

Intelligent testing method for autonomous driving based on interaction of multiple background traffic participants

The present invention relates to a learning-oriented transferable automatic driving method and system driven by a world model. The method includes the following steps: constructing a simulation environment; collecting a large batch of data in the simulation environment, and collecting a small batch of data in the real environment; constructing a world model, and performing model training in the simulation environment by using the large batch of data, wherein the world model takes a camera image as an input to model an environment by using a generative world model; storing and transmitting historical moment information by hidden variables, and outputting an aerial view and a control instruction; and performing domain adaptive transferring training in the real environment on the basis of the small batch of data, and deploying the model in an autonomous vehicle in the real world to achieve virtuality-to-reality transferring general integrated automatic driving.

Learning-oriented transferable automatic driving method and system driven by world model

The present invention provides a multi-load wireless power transfer system based on high-order Anti-PT symmetry. By introducing a 'W-shaped' anti-resonance mode into a basic WPT platform, an effective Anti-PT non-Hermitian system is conveniently constructed. The “level attraction” of the anti-resonance mode is combined with the “level splitting” of the anti-resonance mode and the resonance mode, to study the “level pinning” effect of high-order Anti-PT symmetry. Compared with the traditional resonant WPT, the anti-resonant WPT has higher security, stability, transfer efficiency, and flexibility. Considering the miniaturization and integration of devices, a “meta coil” is designed by using a “synthetic dimension”, and used to construct a high-order Anti-PT symmetric system, thereby achieving multi-load efficient WPT. The new WPT technology based on the “level pinning” effect of high-order Anti-PT symmetry provides a good application research platform for enriching non-Hermitian physics.

Multi-load wireless power transfer system based on high-order anti-PT symmetry

The present invention provides a real-time path planning method taking into account dynamic properties of vehicles; the method includes a calculation without connecting to internet of a reachable set and an online path planning; in the calculation without connecting to internet of the reachable set, all vehicle safety states are traversed by means of the vehicle model and the wheel model, thereby predicting the position set capable of being reached by the vehicle at a next moment; in the online path planning, by means of calculating the position set capable of being reached by the vehicle at the next moment with connecting to internet, non-linear dynamic constraints are provided for the exploration of the artificial potential field method, achieving the purpose of real-time planning while taking into account dynamic properties of the vehicles.

Real-time path planning method for intelligent driving taking into account dynamic properties of high-precision vehicles

The present disclosure relates to a real-time control method for an additional yaw moment of a distributed drive electric vehicle, including the following steps: acquiring and inputting a real-time motion state of the distributed drive electric vehicle into a vehicle dynamics model, using a yaw rate and a sideslip angle of the distributed drive electric vehicle as tracking targets to suppress actuation energy, and performing optimization calculation on an additional yaw moment to acquire an amount of the additional yaw moment distributed for each tire; and in the optimization calculation process, a linear expression of the sideslip angle with respect to the additional yaw moment and a linear expression of the yaw rate with respect to the additional yaw moment are constructed, so as to perform search calculation on the additional yaw moment.

Real-time control method for additional yaw moment of distributed drive electric vehicle

The present disclosure relates to a multi-constraint optimal distribution method for a torque vectoring of a distributed drive electric vehicle, which includes: obtaining a vehicle real-time motion state, taking a pre-constructed tire rotation dynamics model as a control object of an optimal distribution problem, establishing an objective function for tracking a desired additional yaw moment, tracking a desired tire slip ratio, and suppressing a motor output energy consumption to a minimum value, establishing a corresponding system constraint, solving the optimal distribution problem, and obtaining an optimal distribution solution for a torque vectoring of each tire. Compared with the prior art, the present disclosure can effectively track a desired control target, can also effectively constrain an important state and a control input of the vehicle, and can avoid wrong solutions or no solution caused by an optimization problem.

Multi-constraint optimal distribution method for torque vectoring of distributed drive electric vehicle

A coordinated control method for electric vehicles having independent four-wheel driving and steering, comprising the steps of: calculating to obtain a desired value of yaw velocity according to the steering angle and the current vehicle driving speed, and limiting the desired value of yaw velocity according to the current road adhesion condition; constructing an optimization problem according to the current vehicle motion state and the desired value of yaw velocity, and solving the optimization problem to obtain a desired active rear wheel steering angle control variable and a desired additional yaw moment control variable; calculating to obtain an additional torque of each wheel according to a desired additional yaw moment control variable, obtaining a desired active rear wheel steering angle, and sending the additional torque of each wheel and the desired active rear wheel steering angle to an executor of the vehicle for performing a coordinated control.

Coordinated control method for electric vehicles having independent four-wheel driving and steering

Disclosed is a decision-making and planning integrated method for a nonconservative intelligent vehicle in a complex heterogeneous environment, including the following steps: offline establishing and training a social interaction knowledge learning model; obtaining state data of the traffic participants and state data of an intelligent vehicle online in real time, and splicing the state data to obtain an environmental state; using the environmental state as an input to the trained social interaction knowledge learning model to obtain predicted trajectories of all traffic participants including the nonconservative intelligent vehicle; updating the environmental state based on the predicted trajectories; and inputting the updated environmental state to the social interaction knowledge learning model to complete trajectory decision-making and planning for the nonconservative intelligent vehicle by iteration, where a planned trajectory of the nonconservative intelligent vehicle is a splicing result of a first point of a predicted trajectory obtained by each iteration.

Decision-making and planning integrated method for nonconservative intelligent vehicle

The invention relates to a cosmetic composition comprising a low molecular weight silk fibroin and a cosmetically acceptable carrier; wherein said low molecular weight silk fibroin comprises at least 85 parts by weight peptides of molecular weight less than 2500 Da, where amino acid sequence of at least 50 wt % of said peptides is GAGY, GAGAGAGY, GAGVGAGY or AWSSESDF and where said silk fibroin is produced by a process comprising the steps of: (i) mixing an aqueous solution comprising 0.01 to 20 wt % of a high molecular weight silk fibroin of weight average molecular weight 6 to 100 KDa with ochymotrypsin at temperature of 25 to 45° C., under pH of 6 to 9 for 4 to 24 hours; where ratio of said high molecular weight silk fibroin to said ochymotrypsin in said solution is from 100:1 to 300:1 parts by weight: (ii) inactivating excess ochymotrypsin and separating the inactivated α-chymotrypsin from reaction mixture of step (i); and (ill) drying said reaction mixture to obtain said low molecular weight silk fibroin; wherein said composition comprises 0.1 to 10% by weight of the low molecular weight silk fibroin.

Cosmetic compositions comprising low molecular weight silk fibroin

Disclosed is a method for predicting a trajectory of a traffic participant in a complex heterogeneous environment, including the following steps: obtaining traffic participant information in a complex heterogeneous environment; arranging and numbering traffic participant classes based on the class information, to obtain serial numbers of the traffic participant classes; establishing a position graph, a velocity graph, an acceleration graph, and a class graph, into each of which expert experience is introduced; and capturing topological structure relationships and time dependence relationships to obtain a position hidden state, a velocity hidden state, an acceleration hidden state, and a class hidden state; classifying the position hidden state, the velocity hidden state, the acceleration hidden state, and the class hidden state to obtain a hidden state set of traffic participants; and decoding hidden states of the traffic participants separately using a corresponding decoder to obtain future trajectory predictions of the traffic participants.

Method for predicting trajectory of traffic participant in complex heterogeneous environment

A vehicle state estimation method based on adaptive total variation denoising (TVD) filtering includes the following steps: step 1: collection and preprocessing of an original signal of a vehicle; step 2: noise level evaluation, step 3: Teager-Kaiser energy evaluation; step 4: optimization problem construction; and step 5: application of a filtered signal in the step 4 in the estimation of a vehicle state. The vehicle state estimation method is mainly based on the global noise level characteristic and the local intensity change characteristic of the vehicle system state data, and adaptive filtering of parameters is achieved by means of a TVD filtering method. The signal is denoised to the maximum extent, peak information of the signal is retained while the data smoothness is maintained, and then the signal is used for vehicle state estimation, working condition identification and the like.

Vehicle state estimation method based on adaptive total variation denoising filtering

Disclosed is a steering wheel torque feedback optimization control method for a differential braking system, including: estimating a tire lateral force of a vehicle; predicting a state of the vehicle at a next moment under a non-differential torque condition; predicting a state of the vehicle at the next moment under a differential torque condition with an additional yaw moment; calculating tire lateral forces at the next moment under the differential torque condition and the non-differential torque condition separately based on the predicted states of the vehicle at the next moment under the differential torque condition and the non-differential torque condition, and obtaining a predicted one-step change value of a front tire lateral force by performing subtraction; performing integral calculation based on time, and obtaining a continuous change amount of the tire lateral force; and correcting a desired moment of a steering power motor.

Steering wheel torque feedback optimization control method for differential braking system

Disclosed is an intelligent driving test method with corner cases dynamically completed based on human feedback, including the following steps: obtaining an initial state of a real environment; building an original scene driver based on reinforcement learning, and correcting behavior selection to obtain an exploratory behavior; testing in a testing environment, performing expert evaluation, and building a dynamic corner case completion library based on the human feedback; building an imitation learning driver based on the human feedback, updating a policy based on test data in the dynamic corner case completion library, training the imitation learning driver, and outputting a corner case reproduction behavior; obtaining the initial state of the real environment and an initial environmental state of the dynamic corner case completion library, and selecting a scene driver; and outputting a corresponding behavior, and testing in the testing environment based on the corresponding behavior to obtain a test result.

Intelligent driving test method with corner cases dynamically completed based on human feedback

The present disclosure relates to a self-adaptive guided advanced driver assistance system (ADAS) considering a driving skill difference between drivers, including a driving skill classification module, configured to calculate a vehicle stability margin based on a vehicle state, and obtain a corresponding driving skill classification result with the vehicle stability margin and a driver state as inputs of a driving skill classification model; a skill learning range classification module, configured to obtain the vehicle stability margin and a distance between a vehicle and a lane line boundary, and use a skill learning range classification model to obtain a skill learning range classification result; and a self-adaptive guided driving right allocation module, configured to realize driving right allocation control based on the driving skill classification result and the skill learning range classification result, and generate an assisted steering torque acting on a vehicle steering system.

Self-adaptive guided advanced driver assistance system considering driving skill difference between drivers

A longitudinal and lateral vehicle motion cooperative control method based on a fast solving algorithm comprises the following steps: calculating a desired yaw rate according to a steering wheel rotation angle and a current vehicle traveling speed; constructing a nonlinear optimization problem according to the desired yaw rate and a current actual motion state of a vehicle, wherein an objective function of the nonlinear optimization problem is used for tracking the desired yaw rate, and simultaneously for restraining a lateral speed and a tire slip ratio of the vehicle; solving the nonlinear optimization problem to calculate desired slip ratios of four tires; calculating an additional torque of each tire according to an actual slip ratio and the desired slip ratio of the tire; and sending the additional torque of each tire to an actuator of the vehicle for cooperative control.

Longitudinal and lateral vehicle motion cooperative control method based on fast solving algorithm

Growing community of inventors

Shanghai, China

Hong Chen