ArticleSensors (Basel, Switzerland)2017
Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Instantaneous Material Classification Using a Polarization-Diverse RMCW LIDAR.Sensors (Basel, Switzerland) · 2024Article
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
- Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids.Computational intelligence and neuroscience · 2021Article
- An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system.PeerJ. Computer science · 2021Article
- The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots.Computational intelligence and neuroscience · 2021Article
- Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2020Article
- A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption.Computational intelligence and neuroscience · 2020Article
- A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images.Computational intelligence and neuroscience · 2020Article
- Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain.Sensors (Basel, Switzerland) · 2019Article
- Socially Compliant Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports.Sensors (Basel, Switzerland) · 2019Article
- A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints.Sensors (Basel, Switzerland) · 2019Article
- Smart Sensing of Pavement Temperature Based on Low-Cost Sensors and V2I Communications.Sensors (Basel, Switzerland) · 2018Article
- Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.
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Registered trials
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