Junqueira et al., 2021 - Google Patents
A model-less approach for estimating vehicles sideslip angle by a neural network conceptJunqueira et al., 2021
View PDF- Document ID
- 1162625677090708693
- Author
- Junqueira B
- Victorino A
- Baêta J
- Publication year
- Publication venue
- 2021 IEEE International Conference on Mechatronics (ICM)
External Links
Snippet
Over the past few years, vehicle dynamics systems have been constantly improved by new technologies due to the rapid advance in computational systems and, so, have been continually developed to enhance vehicle handling and safety of the passengers …
- 230000001537 neural 0 title abstract description 22
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/172—Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/068—Road friction coefficient
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T2210/00—Detection or estimation of road or environment conditions; Detection or estimation of road shapes
- B60T2210/10—Detection or estimation of road conditions
- B60T2210/12—Friction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Singh et al. | Literature review and fundamental approaches for vehicle and tire state estimation | |
| CN113386781B (en) | Intelligent vehicle track tracking control method based on data-driven vehicle dynamics model | |
| Ribeiro et al. | Estimation of tire–road friction for road vehicles: a time delay neural network approach | |
| Chen et al. | Sideslip angle fusion estimation method of three-axis autonomous vehicle based on composite model and adaptive cubature Kalman filter | |
| CN103434511B (en) | The combined estimation method of a kind of speed of a motor vehicle and road-adhesion coefficient | |
| CN113408047B (en) | Vehicle dynamics prediction model based on time-lag feedback neural network, training data acquisition method and training method | |
| Wang et al. | A review of dynamic state estimation for the neighborhood system of connected vehicles | |
| Ghosh et al. | A deep learning based virtual sensor for vehicle sideslip angle estimation: experimental results | |
| CN112099378B (en) | Front vehicle lateral motion state real-time estimation method considering random measurement time lag | |
| Junqueira et al. | A model-less approach for estimating vehicles sideslip angle by a neural network concept | |
| Da Lio et al. | Robust and sample-efficient estimation of vehicle lateral velocity using neural networks with explainable structure informed by kinematic principles | |
| Liu et al. | Vehicle state and parameter estimation based on double cubature Kalman filter algorithm | |
| Lu et al. | Road adhesion coefficient identification method based on vehicle dynamics model and multi-algorithm fusion | |
| Liu et al. | Vehicle state and parameter estimation based on improved extend Kalman filter | |
| Fouka et al. | Motorcycle state estimation and tire cornering stiffness identification applied to road safety: Using observer-based identifiers | |
| Cao et al. | Vehicle longitudinal and lateral dynamics modeling by deep neural network | |
| Liu et al. | Vehicle state estimation based on unscented kalman filtering and a genetic algorithm | |
| Schäfke et al. | Transformer neural networks for maximum friction coefficient estimation of tire-road contact using onboard vehicle sensors | |
| CN114879700B (en) | A relative motion parameter estimation method for intelligent vehicle platooning driving evaluation | |
| He et al. | Commercial vehicle state estimation based on interactive multi-model square root cubature kalman filtering | |
| Wang et al. | An adaptive estimation of ground vehicle state with unknown measurement noise | |
| Tian et al. | Recent estimation techniques of vehicle-road-pedestrian states for traffic safety: Comprehensive review and future perspectives | |
| Liu et al. | Optimal control of path tracking for vehicle-handling dynamics | |
| El Youssfi et al. | TS fuzzy observers to design actuator fault-tolerant control for automotive vehicle lateral dynamics | |
| Hwang et al. | Hybrid Feature Selection Approach to Finding Optimal Feature Subsets for Vehicle Lateral Velocity Estimation |