1,358 research outputs found
Improving acoustic vehicle classification by information fusion
We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac
A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities
The measurement and provision of precise and upto-date traffic-related key
performance indicators is a key element and crucial factor for intelligent
traffic controls systems in upcoming smart cities. The street network is
considered as a highly-dynamic Cyber Physical System (CPS) where measured
information forms the foundation for dynamic control methods aiming to optimize
the overall system state. Apart from global system parameters like traffic flow
and density, specific data such as velocity of individual vehicles as well as
vehicle type information can be leveraged for highly sophisticated traffic
control methods like dynamic type-specific lane assignments. Consequently,
solutions for acquiring these kinds of information are required and have to
comply with strict requirements ranging from accuracy over cost-efficiency to
privacy preservation. In this paper, we present a system for classifying
vehicles based on their radio-fingerprint. In contrast to other approaches, the
proposed system is able to provide real-time capable and precise vehicle
classification as well as cost-efficient installation and maintenance, privacy
preservation and weather independence. The system performance in terms of
accuracy and resource-efficiency is evaluated in the field using comprehensive
measurements. Using a machine learning based approach, the resulting success
ratio for classifying cars and trucks is above 99%
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
A traffic monitoring system is an integral part of Intelligent Transportation
Systems (ITS). It is one of the critical transportation infrastructures that
transportation agencies invest a huge amount of money to collect and analyze
the traffic data to better utilize the roadway systems, improve the safety of
transportation, and establish future transportation plans. With recent advances
in MEMS, machine learning, and wireless communication technologies, numerous
innovative traffic monitoring systems have been developed. In this article, we
present a review of state-of-the-art traffic monitoring systems focusing on the
major functionality--vehicle classification. We organize various vehicle
classification systems, examine research issues and technical challenges, and
discuss hardware/software design, deployment experience, and system performance
of vehicle classification systems. Finally, we discuss a number of critical
open problems and future research directions in an aim to provide valuable
resources to academia, industry, and government agencies for selecting
appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces
Underwater Localization in a Confined Space Using Acoustic Positioning and Machine Learning
Localization is a critical step in any navigation system. Through localization, the vehicle can estimate its position in the surrounding environment and plan how to reach its goal without any collision. This thesis focuses on underwater source localization, using sound signals for position estimation. We propose a novel underwater localization method based on machine learning techniques in which source position is directly estimated from collected acoustic data. The position of the sound source is estimated by training Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Convolutional Neural Network (CNN). To train these data-driven methods, data are collected inside a confined test tank with dimensions of 6m x 4.5m x 1.7m. The transmission unit, which includes Xilinx LX45 FPGA and transducer, generates acoustic signal. The receiver unit collects and prepares propagated sound signals and transmit them to a computer. It consists of 4 hydrophones, Red Pitay analog front-end board, and NI 9234 data acquisition board. We used MATLAB 2018 to extract pitch, Mel-Frequency Cepstrum Coefficients (MFCC), and spectrogram from the sound signals. These features are used by MATLAB Toolboxes to train RF, SVM, FNN, and CNN. Experimental results show that CNN archives 4% of Mean Absolute Percentage Error (MAPE) in the test tank. The finding of this research can pave the way for Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) navigation in underwater open spaces
Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners
This work proposes to use passive acoustic perception as an additional
sensing modality for intelligent vehicles. We demonstrate that approaching
vehicles behind blind corners can be detected by sound before such vehicles
enter in line-of-sight. We have equipped a research vehicle with a roof-mounted
microphone array, and show on data collected with this sensor setup that wall
reflections provide information on the presence and direction of occluded
approaching vehicles. A novel method is presented to classify if and from what
direction a vehicle is approaching before it is visible, using as input
Direction-of-Arrival features that can be efficiently computed from the
streaming microphone array data. Since the local geometry around the
ego-vehicle affects the perceived patterns, we systematically study several
environment types, and investigate generalization across these environments.
With a static ego-vehicle, an accuracy of 0.92 is achieved on the hidden
vehicle classification task. Compared to a state-of-the-art visual detector,
Faster R-CNN, our pipeline achieves the same accuracy more than one second
ahead, providing crucial reaction time for the situations we study. While the
ego-vehicle is driving, we demonstrate positive results on acoustic detection,
still achieving an accuracy of 0.84 within one environment type. We further
study failure cases across environments to identify future research directions.Comment: Accepted to IEEE Robotics & Automation Letters (2021), DOI:
10.1109/LRA.2021.3062254. Code, Data & Video:
https://github.com/tudelft-iv/occluded_vehicle_acoustic_detectio
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