Tongji University

Autoliv Development Ab

Schneider Electric It Corporation

Qiang Meng

Hong Chen

Lin Zhang

Rongjie Yu

Jinlong Hong

Ting Wang

Bin Li

Peng Yuan

Jianfei Ma

Bo Hong

Qiang Li

Rong Long

Jinhui Wang

Min Jin

Yonghai Du

Yujian Fan

Yingrong Huang

Qiang Lqli

Yanjun Huang

Haoqi Hu

Jiujian Ni

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

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

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

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

An automotive front seat overhead airbag includes a supporting air chamber and a protective air chamber mounted on a roof. The supporting air chamber is inflated from the roof towards a dashboard upon inflation. The protective air chamber is in communication with the supporting air chamber. After the supporting air chamber is inflated, the protective air chamber is inflated from the supporting air chamber towards the human body. A first protective space is reserved between the supporting air chamber and the protective air chamber on a side facing the human body. The first protective space corresponds to the head of the human body for protecting the head and neck of the human body. An automobile includes a roof and a dashboard, and further includes the automotive front seat overhead airbag described above. An upper end of the supporting air chamber is connected to a gas generator.

Automotive front seat overhead airbag and automobile

A safety airbag having an air bag, the air bag configured with an inflated state and a stowed state, and including a main body portion and a first side flap and a second side flap connected to two sides of the main body portion. The first side flap and the second side flap extend from the main body portion towards an occupant when the air bag is in the inflated state such that the head of the occupant is accommodated in a recessed portion between the first side flap and the second side flap. The first side flap includes a first inner side surface and a first outer side surface. The second side flap includes a second inner side surface and a second outer side surface.

Airbag and vehicle

The disclosure relates to a method for real time optimization and parallel computing of model prediction control based on a computing chart, comprising the following steps: building a prediction model of a system state amount and building a target function of a system; building a parallel computing architecture for model prediction control of a prediction model and the target function and employing a triggering parallel computing method by the parallel computing architecture to synchronously compute the prediction model and the target function; and solving and computing a gradient with a manner of back propagation and using a gradient descent method to optimize a control amount of the system and realize real time optimal control of the system. Compared with the prior art, the present disclosure greatly improves a computing efficiency, ensures real time property of a model prediction controller, and extends application fields of model prediction control.

Method for real time optimization and parallel computing of model prediction control based on computing chart

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

Parallel computing method for man-machine coordinated steering control of smart vehicle based on risk assessment

A receiver of a cooling unit includes a cylindrical body having a cylindrical wall that defines an interior chamber, a bottom wall formed with the cylindrical wall, and a top wall formed with the cylindrical wall. The receiver further includes an inlet provided in the cylindrical body, an outlet provided in the cylindrical body, a heater well disposed within the cylindrical body, and a heater positioned in the heater well to selectively heat a heat transfer fluid contained within the interior chamber of the cylindrical body. The heater well can be configured to extend from the top wall to adjacent the bottom wall along an axis that is coaxial with an axis of the cylindrical wall of the cylindrical body or extend horizontally adjacent the bottom wall of the cylindrical body.

Receiver for cooling system

An air containment system () is configured to span an aisle () formed by two rows of racks () or by one row of racks () and a wall () to form a ceiling. The air containment system () includes a frame structure () configured to be supported by the two rows of racks () or by the one row of racks () and the wall (). The air containment system () further includes at least one ceiling panel () releasably supported by the frame structure (), and a lock assembly () coupled to the frame structure () and configured to release the at least one ceiling panel () upon detecting a predetermined condition. Embodiments of the air containment system () and methods for releasing ceiling panels () are further disclosed.

Growing community of inventors

Shanghai, China

Qiang Meng