Nio Usa, Inc.

Integrated Silicon Solution Incorporated

Nio Technology (anhui) Co., Ltd.

Albert Wu

Ling Chen

Tien-Ler Lin

Zhongkui Wang

Arek V Sredzki

Keja S A Rowe

Omid Ghasemalizadeh

Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

Systems and methods of autonomously controlling vehicle states

Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

Vehicle control system using nonlinear dynamic model states and steering offset estimation

Systems of an electrical vehicle and the operations thereof are provided. A vehicle control system of a vehicle determines a range of lane change initiation times. For a plurality of lane change initiation times in the range of lane change initiation times the vehicle control system determines a set of reachable states. The set of reachable states comprises a plurality of reachable states. For one or more reachable states of the plurality of reachable states the vehicle control system computes an ensuing trajectory. The vehicle control system compares each of the computed ensuing trajectories. Based on the comparison of the computed ensuing trajectories the vehicle control system determines an optimal lane change maneuver. The vehicle control system executes the optimal lane change maneuver by controlling the vehicle from a first lane to a second lane.

Systems and methods of autonomously controlling vehicle lane change maneuver

Disclosed is a flash EEPROM cell needing only a 5 volt external source using an on-chip voltage multiplier circuit to provide high voltages necessary to effect Fowler-Nordheim tunneling during both the program and erase modes. Properties of dielectric layers between a floating gate and a control gate and between the floating gate and a drain region differ to facilitate programming and erasing of the floating gate. Also disclosed is a method for producing a flash EEPROM cell by forming the insulative layer between a floating gate and a control gate to have a capacitance lower than the capacitance of the insulating layer between the floating gate and a drain region.

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San Jose, CA, United States of America

Albert Wu