A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials

Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. Four urban arterials in Orlando, FL are selected to conduct a case study. Different types of data are utilized to predict the crash risk, including traffic data, signal timing data, and weather data. Various data preparation techniques are applied also. In addition, the synthetic minority over-sampling technique (SMOTE) is used for oversampling the crash cases to address the data imbalance issue. The LSTM-CNN is fine-tuned on the training data and validated on the test data via different metrics. In the end, five other benchmarks models are also developed for model comparison, including Bayesian Logistics Regression, XGBoost, LSTM, CNN, and Sequential LSTM-CNN. Experimental results suggest that the proposed LSTM-CNN outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this thesis indicate the promising performance of using LSTM-CNN to predict real-time crash risk at arterials

Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Development of an object detection and mask generation software for dynamic beam projection in automotive pixel lighting applications

Nowadays there are many contributions to the automotive industry and the field is developing fast. This work can be used for some real-time autonomous driving applications. The goal was to add advanced functionality to a standard light source in collaboration with electronic systems. Including advanced features may result in safer and more pleasant driving. The application fields of the work could include glare-free light sources, orientation and lane lights, marking lights, and symbol projection. On a real-time source, object detection and classification with a confidence score is implemented. The best model is obtained by intending to train the model with varying parameters. The most accurate result which is mAP value 0.572 was obtained by distributing the training dataset with learning rate 0.2 and setting the epochs to 300. Moreover, a basic implementation of a glare-free light source was done to avoid the drivers from being blinded by the illumination of the beams. The car and rectangle shape masks were generated as image files and sent as CSV files to the pixel light source device. As a result, the rectangle shaped mask functions more precisely then car shaped.Nowadays there are many contributions to the automotive industry and the field is developing fast. This work can be used for some real-time autonomous driving applications. The goal was to add advanced functionality to a standard light source in collaboration with electronic systems. Including advanced features may result in safer and more pleasant driving. The application fields of the work could include glare-free light sources, orientation and lane lights, marking lights, and symbol projection. On a real-time source, object detection and classification with a confidence score is implemented. The best model is obtained by intending to train the model with varying parameters. The most accurate result which is mAP value 0.572 was obtained by distributing the training dataset with learning rate 0.2 and setting the epochs to 300. Moreover, a basic implementation of a glare-free light source was done to avoid the drivers from being blinded by the illumination of the beams. The car and rectangle shape masks were generated as image files and sent as CSV files to the pixel light source device. As a result, the rectangle shaped mask functions more precisely then car shaped

Traffic Analysis from Video

V rámci této práce byl navržen a implementován systém pro analýzu dopravy z videa. Tento system umožňuje detekovat, sledovat a klasifikovat automobily. Systém je schopný detekovat pruhy z pohybu projíždějících automobilů a také je možné určit, zdali daný automobil jede v protisměru. Rychlost projíždějících automobilů je také měřena. Pro funkčnost systému není vyžadován žadný manuální vstup nebo kalibrace kamery, jelikož kamera je plně automacky zkalibrována pomocí úběžníků. Navržený systém pracuje s velkou přesností detekce, sledování a klasifikace automobilů a také rychlost automobilů je měřena s~malou chybou. Systém je schopný pracovat v reálném čase a je aktuálně využíván pro nepřetržité online sledování dopravy. Největším přínosem této práce je plně automatické měření rychlostí projíždějích vozidel.A system for traffic analysis was designed and implemented during work on this thesis. The system is able to detect, track and classify vehicles. Also, the system is able to detect lanes or determine whether a vehicle is passing in wrong way. The speed of observed vehicles is also measured. The system does not require any manual input or calibration whatsoever as the video camera is fully automatically calibrated by detected vanishing points. The accuracy of the detection, tracking and classification is high and the speed of vehicles is measured with a low error. The system runs in real time and it is currently used for a~continuous monitoring of traffic. The main contribution of the thesis is the fully automated speed measurement of passing vehicles.