Development case study of the first estonian self-driving car, iseauto

The rapid development of intelligent control technology has also brought about changes in the automotive industry and led to development of autonomous or self-driving vehicles. To overcome traffic and environment issues, self-driving cars use a number of sensors for vision as well as a navigation system and actuators to control mechanical systems and computers to process the data. All these points make a self-driving car an interdisciplinary project that requires contribution from different fields. In our particular case, four different university departments and two companies are directly involved in the self-driving car project. The main aim of the paper is to discuss the challenges faced in the development of the first Estonian self-driving car. The project implementation time was 20 months and the project included four work packages: preliminary study, software development, body assembly and system tuning/testing of the self-driving car. This paper describes the development process stages and tasks that were distributed between the sub-teams. Moreover, the paper presents the technical and software solutions that were used to achieve the goal and presents a self-driving last mile bus called ISEAUTO. Special attention is paid to the discussion of safety challenges that a self-driving electrical car project can encounter. The main outcomes and future research possibilities are outline

Development case study of first Estonian Self-driving car ISEAUTO

Rapid development of intelligent control technics has brought also changes in automotive industry and led to development of autonomous or self-driving vehicles. To overcome traffic and environment issues self-driving cars use a number of sensors for vision and navigation system, actuators to control mechanical systems and computers to process the data. All these points make a self-driving car an interdisciplinary project that requires contribution form the different fields. In our particular case four different university departments and two companies are directly involved into self-driving car project. The main aim of the paper is to discuss the challenges faced in development of first Estonian self-driving car. The project realization time was 20 months for four work packages: preliminary study, software development, body assembly and systems tuning/testing of the self-driving car. This paper describes a development process stages and tasks that were spread between sub-teams. Moreover, a paper present technical and software solution that were used to achieve the goal and present a self-driving last mile bus called ISEAUTO. Special attention is putted on discussion of safety challenges that self-driving electrical car project can meet. Main outcomes and future research possibilities are outlined

Resource-Aware Scene Text Recognition Using Learned Features, Quantization, and Contour-Based Character Extraction

Scene texts serve as valuable information for humans and autonomous systems to make informed decisions. Processing scene texts poses significant difficulties for computer systems due to several factors, primarily due to variations in image characteristics. These factors make it very challenging for computer systems to accurately detect and interpret scene texts, despite being easily understandable to humans. To address this problem, scene text detection and recognition methods leverage computer vision and/or deep learning methods. Deep learning methods require substantial resources, including computing power, memory, and energy. As such, their use in real-time embedded applications, particularly those that run on integer-only hardware, is very challenging due to the resource-intensive nature of these methods. In this paper, we developed an approach to address this challenge and to showcase its effectiveness, we trained end-to-end models for shipping container number detection and recognition. By doing so, we were able to demonstrate the accuracy and reliability of our proposed method for processing scene texts on integer-only hardware. Our efforts to optimize the models yielded impressive results. We reduced the model size by a factor of 3.8x without significantly affecting the models’ performance. Moreover, the optimized models were 1.6x faster, and the maximum RAM usage was 6.6x lower than the base models. These results demonstrate the efficiency and practicality of our approach for scene text processing on integer-only embedded hardware

Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme

The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications

Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers