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Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing [article]

Noha Radwan, Wera Winterhalter, Christian Dornhege, Wolfram Burgard
2017 arXiv   pre-print
Our approach solely relies on laser and radar data and learns a classifier based on Random Forests to predict when it is safe to cross the road.  ...  In this work, we propose a novel multi-modal learning approach for the problem of autonomous street crossing.  ...  CONCLUSIONS In this paper we presented a novel approach based on Random Forests for learning to predict when it is safe to cross a street.  ... 
arXiv:1709.06039v1 fatcat:5cpxarxpnbguxolqny6hjmljry

Dynamic Traffic Light Control

2020 International journal of recent technology and engineering  
The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).  ...  The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment.  ...  Deep learning approaches such as SSD, Faster R-CNN, YOLO-v3, R-FCN are utilized to detect the object based on the collected dataset [6] .  ... 
doi:10.35940/ijrte.f8609.038620 fatcat:4l2vnhnn4zdxpkktuvxyu7dvsq

Brake Light Detection Algorithm for Predictive Braking

Jesse Pirhonen, Risto Ojala, Klaus Kivekäs, Jari Vepsäläinen, Kari Tammi
2022 Applied Sciences  
This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles.  ...  The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding.  ...  The hybrid approach of colorspace preprocessing and a simple random forest classifier showed a notable improvement when compared to processing with only a random forest classifier.  ... 
doi:10.3390/app12062804 fatcat:3omx6qxrzrhprgywxgym32qa3m

Vehicle Detection in Aerial Images Using Rotation-Invariant Cascaded Forest

Bodi Ma, Zhenbao Liu, Feihong Jiang, Yuehao Yan, Jinbiao Yuan, Shuhui Bu
2019 IEEE Access  
Then, the rotation invariant descriptors are fed into the cascaded forest based on auto-context for feature learning and classification.  ...  INDEX TERMS Rotation invariant, vehicle detection, cascaded forest, aerial images.  ...  [13] explored the deep forest model based on random forest. The performance of deep forest approach on classification is verified by a series of experiments.  ... 
doi:10.1109/access.2019.2915368 fatcat:nak3maahmnei7k7kxgv4tzhjuq

A Hybrid Data-driven Model for Intrusion Detection in VANET

Hind Bangui, Mouzhi Ge, Barbora Buhnova
2021 Procedia Computer Science  
The proposed approach mainly uses the advantages of Random Forest to detect known network intrusions.  ...  The proposed approach mainly uses the advantages of Random Forest to detect known network intrusions.  ...  This is because our model is composed of two phases, where the first one corresponds to the misuse detection approach in IDS, and the second phase corresponds to the anomaly detection approach that detects  ... 
doi:10.1016/j.procs.2021.03.065 fatcat:vjpxfi4qgzd3rkhwet4s5hdcdu

Automatic Number Plate Recognition Using Image Processing

Hrithik Roshan Palampatla
2021 International Journal for Research in Applied Science and Engineering Technology  
ANPR is often used in the detection of stolen vehicles, traffic surveillance system.  ...  The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images.  ...  Implementation of Random Forest modelWe have used Random forest classifier from Scikit learn library to train a random forest model.  ... 
doi:10.22214/ijraset.2021.36889 fatcat:vy3bmxnq25cy5eleh5jvy4sb2u

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

José Manuel Lozano Domínguez, Faroq Al-Tam, Tomás de J. Mateo Sanguino, Noélia Correia
2020 Sensors  
Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models.  ...  A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned.  ...  forest; SSD: single shot detector; SOM: self-organizing map; SRS: smart road safety; TPR: true positive rate; YOLO: you-only-look-once.  ... 
doi:10.3390/s20216019 pmid:33114001 pmcid:PMC7660295 fatcat:muxeiou3a5guzerzpmosfcs7de

Review on Estimation of Road Quality using Mobile Sensors & Machine Learning Techniques

Trupti K Dange
2020 Bioscience Biotechnology Research Communications  
The availability of increased computational power and collection of the massive amount of data have redefined the value of the machine learning-based approaches for addressing the emerging demands and  ...  Using AI based Machine Learning Algorithms can detect the road anomalies, analyse them, share this information to the users while driving and also repair them by sending the relevant information to the  ...  Knn, naïve Bayes and Random forest tree) to prepare and test information received from smartphone sensors.  ... 
doi:10.21786/bbrc/13.14/55 fatcat:xlcfwttxjfco5pb3yicz4c4uaa

A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Abhilash Sonker, R. K. Gupta
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique.  ...  The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random  ...  Random forest with 98.03% accuracy in type4 attack, random forest with 95.56% in type8 attack, and random forest with 95.55% is obtained.  ... 
doi:10.11591/ijece.v11i3.pp2535-2547 fatcat:2uw4nxcqhfgpdaofdd35waj2jm

A REVIEW ON ROAD ACCIDENT DETECTION USING DATA MINING TECHNIQUES

Arun Prasath N
2018 International Journal of Advanced Research in Computer Science  
Comparison based on parameters is also done to prove the efficiency of the various road detection techniques and approaches. The comparison result shows the best road accident detection method.  ...  The main focus of this survey is to provide an overview of the literature in road accident detection with various techniques and approaches implemented in them, their merits and demerits etc.  ...  For automatic road detection, a novel approach [15] was proposed. The novel approach was based on detection of damage vehicles from the collected footage from surveillance cameras.  ... 
doi:10.26483/ijarcs.v9i2.5708 fatcat:dt3qbinxwvh6ldmp57z6nwdvky

Traffic Signs Detection Using Machine Learning Algorithms

Yugam Bajaj and Shallu Bashambu
2020 International journal of modern trends in science and technology  
We use Machine Learning Classification Algorithms like k-Nearest Neighbors, Random Forest and Support Vector Machine on our dataset, to compute the best accuracies in the process as well.  ...  With the rapid advancement and developments in the Automobile industry, that day is not far when each of us would be owning their own Autonomous Vehicle.  ...  Random Forest Random forest, like its name implies, consists of a large number of individual decision trees that operate as an ensemble.  ... 
doi:10.46501/ijmtst061119 fatcat:vosxmatei5fg7cphlgulhhf654

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Donato Impedovo, Fabrizio Balducci, Vincenzo Dentamaro, Giuseppe Pirlo
2019 Sensors  
These classic machine learning approaches are compared with the deep learning techniques.  ...  Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation.  ...  In the classic approach (visual features and machine learning classifiers), the Random Forest has gained 84% of accuracy while the deep learning approach has reached an accuracy of over 98% with the same  ... 
doi:10.3390/s19235213 pmid:31795080 fatcat:66pg66mpujbmldgxykpmahmnvy

Vehicle Remote Health Monitoring and Prognostic Maintenance System

Uferah Shafi, Asad Safi, Ahmad Raza Shahid, Sheikh Ziauddin, Muhammad Qaiser Saleem
2018 Journal of Advanced Transportation  
The approach is produced with the end goal of expanding vehicle up-time and was demonstrated on 70 vehicles of Toyota Corolla type.  ...  Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, K Nearest Neighbor, and Random Forest.  ...  , Nearest Neighbor, and Random Forest/Bagging Tree on different systems of vehicles.  ... 
doi:10.1155/2018/8061514 fatcat:6ef5cxjhfvctxkayt642wxuity

Automotive ECU data-based Driver's Propensity Learning using Evolutionary Random Forest

Jong-Hyun Lee, Sangmin Lim, Chang Wook Ahn
2019 IEEE Access  
INDEX TERMS Big data learning, evolutionary computation, machine learning, evolutionary random forest, driving propensity recognition, vehicle safety assistant systems.  ...  This paper presents an evolutionary machine learning algorithm for recognizing driver's propensity by effectively learning a vast amount of ECU sensor data in the vehicle, and its performance is verified  ...  DANGEROUS DRIVING DETECTION SYSTEM USING EVOLUTIONARY RANDOM FOREST This section introduces a service application of the proposed approach.  ... 
doi:10.1109/access.2019.2911704 fatcat:uilayeeavna6lfqpj5plwskbfa

Anomaly Detection in Intra-Vehicle Networks [article]

Ajeet Kumar Dwivedi
2022 arXiv   pre-print
This paper discusses the security issues of the CAN bus protocol and proposes an Intrusion Detection System (IDS) that detects known attacks on in-vehicle networks.  ...  However, a primary challenge in the automotive industry is to make the vehicle safe and reliable; particularly with the loopholes in the existing traditional protocols, cyber-attacks on the vehicle network  ...  However, Learning-based anomaly detection may be a viable detection approach since it can learn from examples and dynamically respond to the CAN environment independent of protocol, vehicle model, or other  ... 
arXiv:2205.03537v1 fatcat:b5n6lewgorb5nopqs3bsekwbd4
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