Hirz et al., 2018 - Google Patents
Sensor and object recognition technologies for self-driving carsHirz et al., 2018
View PDF- Document ID
- 11158927499436920204
- Author
- Hirz M
- Walzel B
- Publication year
- Publication venue
- Computer-aided design and applications
External Links
Snippet
Autonomous driving functions for motorized road vehicles represent an important area of research today. In this context, sensor and object recognition technologies for self-driving cars have to fulfill enhanced requirements in terms of accuracy, unambiguousness …
- 238000001514 detection method 0 abstract description 21
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
- G01C21/36—Input/output arrangements of navigation systems
-
- 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
- G06K9/00791—Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
- G06K9/00805—Detecting potential obstacles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hirz et al. | Sensor and object recognition technologies for self-driving cars | |
| Liu et al. | A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook | |
| Yu et al. | A study on recent developments and issues with obstacle detection systems for automated vehicles | |
| CN113196357B (en) | Method and system for controlling autonomous vehicles in response to detecting and analyzing strange traffic signs | |
| US10849543B2 (en) | Focus-based tagging of sensor data | |
| Haris et al. | Obstacle detection and safely navigate the autonomous vehicle from unexpected obstacles on the driving lane | |
| Li et al. | A survey of ADAS perceptions with development in China | |
| Ignatious et al. | Analyzing factors influencing situation awareness in autonomous vehicles—a survey | |
| Zhang et al. | A cognitively inspired system architecture for the Mengshi cognitive vehicle | |
| So et al. | Analysis on autonomous vehicle detection performance according to various road geometry settings | |
| Waizman et al. | Micro-simulation model for assessing the risk of vehicle–pedestrian road accidents | |
| Hou et al. | Development of collision avoidance system for multiple autonomous mobile robots | |
| US20210382560A1 (en) | Methods and System for Determining a Command of an Occupant of a Vehicle | |
| Chamola et al. | Overtaking mechanisms based on augmented intelligence for autonomous driving: Data sets, methods, and challenges | |
| US20200219389A1 (en) | Visualiziing real-time intersection occupancy and calculated analytics in 3d | |
| Khatab et al. | Evaluation of 3D vulnerable objects’ detection using a multi-sensors system for autonomous vehicles | |
| CN107907886A (en) | Driving condition recognition method, device, storage medium and terminal equipment | |
| Rana et al. | The perception systems used in fully automated vehicles: a comparative analysis | |
| Deo et al. | Centralised and decentralised sensor fusion-based emergency brake assist | |
| Stäcker et al. | RC-BEVFusion: A plug-in module for radar-camera bird’s eye view feature fusion | |
| Ahmed et al. | Lane marking detection using LiDAR sensor | |
| Shikishima et al. | PMOD-Net: point-cloud-map-based metric scale obstacle detection by using a monocular camera | |
| US12014555B2 (en) | Vehicle localization based on lane templates | |
| Vu et al. | Feature mapping and state estimation for highly automated vehicles | |
| Jain et al. | Autonomous driving systems and experiences: A comprehensive survey |