Sentieiro, 2022 - Google Patents
Machine Learning for Autonomous Vehicle Route Planning and OptimizationSentieiro, 2022
View PDF- Document ID
- 6694348277866961060
- Author
- Sentieiro H
- Publication year
- Publication venue
- Journal of AI-Assisted Scientific Discovery
External Links
Snippet
In the last decade of the 20th and the first decade of the 21st century, the use of autonomous driving products and systems did not pass more than cases that were developed for special operations such as military or space missions. The starting point of autonomous driving for …
- 238000010801 machine learning 0 title description 57
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/20—Instruments for performing navigational calculations
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bachute et al. | Autonomous driving architectures: insights of machine learning and deep learning algorithms | |
US11726477B2 (en) | Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout | |
Yurtsever et al. | A survey of autonomous driving: Common practices and emerging technologies | |
Casas et al. | Mp3: A unified model to map, perceive, predict and plan | |
Badue et al. | Self-driving cars: A survey | |
Sauer et al. | Conditional affordance learning for driving in urban environments | |
Masmoudi et al. | A reinforcement learning framework for video frame-based autonomous car-following | |
Xia et al. | Parameterized Decision-making with Multi-modal Perception for Autonomous Driving | |
Verma et al. | Vehicle detection, tracking and behavior analysis in urban driving environments using road context | |
Gómez-Huélamo et al. | How to build and validate a safe and reliable Autonomous Driving stack? A ROS based software modular architecture baseline | |
Huang et al. | Recoat: A deep learning-based framework for multi-modal motion prediction in autonomous driving application | |
Khanum et al. | Involvement of deep learning for vision sensor-based autonomous driving control: a review | |
US12037011B2 (en) | Method and system for expanding the operational design domain of an autonomous agent | |
Yu et al. | LF-Net: A Learning-based Frenet Planning Approach for Urban Autonomous Driving | |
Sentieiro | Machine Learning for Autonomous Vehicle Route Planning and Optimization | |
YU et al. | Vehicle Intelligent Driving Technology | |
Zhao et al. | A Survey on Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions | |
Malik et al. | Explainable Artificial Intelligence for Autonomous Vehicles: Concepts, Challenges, and Applications | |
Başaran | Deep Learning for Autonomous Vehicle Route Optimization in Rural Areas | |
Xia et al. | Parameterized Decision-Making with Multi-Modality Perception for Autonomous Driving | |
Zhong | Occlusion-aware Perception and Planning for Automated Vehicles | |
Soman | Learning-based Stochastic Model Predictive Control for Autonomous Driving | |
Misra et al. | Machine learning for autonomous vehicles | |
Ghintab et al. | PID-like IT2FLC-Based Autonomous Vehicle Control in Urban Areas | |
García | Machine Learning for Enhancing Autonomous Vehicle Decision-Making in Urban Environments |