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AbstractIntelligent Transportation Systems (ITS) are gaining momentum due to the advantages it possesses in congestion-free traffic, reduced probability of accidents, and economical transit thus reducing the transit time, saving human life, and helping the growth of the economy. Autonomous vehicles (AV) are an important part of ITS as these are the actual actuators of the ITS. In AVs, the perception system is particularly important as this provides vital information to the motion and planning system. Any error in perception of the environment by the AV will lead to the failure of the entire ITS. This article provides a thorough review of the recent technological advancements in perception systems. In this work, we have considered peer-reviewed journals and conference proceedings on AV from 2019 onwards. The primary focus is on motion prediction models, object detection, localization, sensor data fusion, sensors, autonomous driving (AD), communication technology and AI. This article also discusses the various challenges faced by perception systems in AD and how communication technology and Artificial Intelligence (AI) can help the perception system to overcome the existing challenges.

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics. [ABSTRACT FROM AUTHOR]

To improve the operational safety of rail transportation equipment for mountainous transmission lines in forest areas, the safety detection system of rail transportation equipment based on image recognition was established. The electronic control system of rail transportation equipment for mountainous transmission lines in the forest areas was provided, and the various sensors used in the perception module system were introduced. The Faster-RCNN algorithm based on the combination of split-attention networks and self-calibration convolutions was applied to obtain better feature extraction, and the improved Faster- RCNN algorithm was used for the surrounding personnel recognition experiments. The remote control software of rail transportation equipment for transmission lines was developed based on QT, and the remote control of the equipment was realized. The results show that the improved Faster-RCNN algorithm can significantly improve the accuracy of identifying personnel around equipment in strong lighting and complex environments of forest areas. The mean average precision of image recognition can reach 87.13%, which is 74.35% higher than conventional Faster-RCNN and 76.28% higher than cascaded Faster-RCNN. The results fully prove that the improved Faster-RCNN algorithm has excellent recognition ability and ensures the safe operation of railway transportation equipment for mountainous transmission lines in forest areas. [ABSTRACT FROM AUTHOR]

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle's own as well as surrounding objects' relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe. [ABSTRACT FROM AUTHOR]

Safe and reliable environmental perception is crucial for the highly automated or even autonomous operation of agriculture machines. However, despite the growing importance of robust perception systems, there is a lack of research on developing a generalized approach to designing and assessing their functional safety and reliability. This article describes the design and verification for the implementation of a functionally safe and reliable perception in a generalized manner. The normative references, ISO 25119, ISO 21448, and ISO 18497, which pertain to the functional safety design of highly automated mobile machines and vehicles, are introduced. This article explains the concept and considerations for designing a perception system. A new criterion ''perception density'' is proposed as a new standard unit to evaluate the spatial resolution of different types of perception sensors. To verify the performance of perception sensors, test scenarios were designed to address practical working conditions, including obstacles to be detected, foreseeable obscurity, test sites, and various lighting conditions. The test results were analysed and evaluated in light of the predetermined design considerations, and the perception sensors were quasi-numerically evaluated using the evaluation procedure.

In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

Spike-based perception brings up a new research idea in the field of neuromorphic engineering. A high-performance biologically inspired flexible spiking neural network (SNN) architecture provides a novel method for the exploration of perception mechanisms and the development of neuromorphic computing systems. In this article, we present a biological-inspired spike-based SNN perception digital system that can realize robust perception. The system employs a fully paralleled pipeline scheme to improve the performance and accelerate the processing of feature extraction. An auditory perception system prototype is realized on ten Intel Cyclone field-programmable gate arrays, which can reach the maximum frequency of 107.28 MHz and the maximum throughput of 5364 Mbps. Our design also achieves the power of 5. 148 W/system and energy efficiency of 845.85 μ J. Our auditory perception implementation is also proved to have superior robustness compared with other SNN systems. We use TIMIT digit speech in noise in accuracy testing. Result shows that it achieves up to 85.75% speech recognition accuracy under obvious noise conditions (signal-to-noise ratio of 20 dB) and maintain small accuracy attenuation with the decline of the signal-to-noise ratio. The overall performance of our proposed system outperforms the state-of-the-art perception system on SNN. [ABSTRACT FROM AUTHOR]

The intelligent tourism service system will help strengthen the management of scenic spots, improve tourism efficiency, and help improve the tourism ecological environment. At present, there are few researches on intelligent tourism service system. This paper attempts to sort out the literature and build structural equation model based on UTAUT2 model (UTAUT is short for Unified Theory of Acceptance and Use of Technology) to analyze the factors that affect the users' willingness of use the intelligent tourism service system (ITSS) in scenic spots. The results show that (1) the effects of the factors affecting the users' intention to use the ITSS of tourist attractions are facilitating conditions (FC), social influence (SI), performance expectation (PE), and effort expectation (EE), (2) Both PE and EE can directly affect the user's intention to use ITSS, while EE indirectly affects the user's intention through PE. (3) SI and FC have a direct impact on the UI of ITSS. The simplicity of use on intelligent tourism application system products can significantly affect the user satisfaction index and product loyalty of the users. In addition, the usefulness factor of perception system and the risk factor of user perception system coexist, with the synergistic effect positively affects the ITSS and use behavior of the whole scenic spot. The main results provide theoretical basis and empirical support for the sustainable and efficient development of ITSS. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are excellent tools with extensive demand. During the last phase of landing, they require additional support to that of GPS. This can be achieved through the UAV's perception system based on its on-board camera and intelligence, and with which decisions can be made as to how to land on a platform (target). A cognitive computation approach is proposed to recognize this target that has been specifically designed to translate human reasoning into computational procedures by computing two probabilities of detection which are combined considering the fuzzy set theory for proper decision-making. The platform design is based on: (1) spectral information in the visible range which are uncommon colors in the UAV's operating environments (indoors and outdoors) and (2) specific figures in the foreground, which allow partial perception of each figure. We exploit color image properties from specific-colored figures embedded on the platform and which are identified by applying image processing and pattern recognition techniques, including Euclidean Distance Smart Geometric Analysis, to identify the platform in a very efficient and reliable manner. The test strategy uses 800 images captured with a smartphone onboard a quad-rotor UAV. The results verify the proposed method outperforms existing strategies, especially those that do not use color information. Platform recognition is also possible even with only a partial view of the target, due to image capture under adverse conditions. This demonstrates the effectiveness and robustness of the proposed cognitive computing-based perception system. [ABSTRACT FROM AUTHOR]

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models’ tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various “similarity overlap threshold” parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle's vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors. [ABSTRACT FROM AUTHOR]

In state-of-the-art deep learning for object recognition, Softmax and Sigmoid layers are most commonly employed as the predictor outputs. Such layers often produce overconfidence predictions rather than proper probabilistic scores, which can thus harm the decision-making of ‘critical’ perception systems applied in autonomous driving and robotics. Given this, we propose a probabilistic approach based on distributions calculated out of the Logit layer scores of pre-trained networks which are then used to constitute new decision layers based on Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) inference. We demonstrate that the hereafter called ML and MAP layers are more suitable for probabilistic interpretations than Softmax and Sigmoid-based predictions for object recognition. We explore distinct sensor modalities via RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5 datasets, where our approach shows promising performance compared to the usual Softmax and Sigmoid layers, with the benefit of enabling interpretable probabilistic predictions. Another advantage of the approach introduced in this paper is that the so-called ML and MAP layers can be implemented in existing trained networks, that is, the approach benefits from the output of the Logit layer of pre-trained networks. Thus, there is no need to carry out a new training phase since the ML and MAP layers are used in the test/prediction phase. The Classification results are presented using reliability diagrams, while detection results are illustrated using precision-recall curves.

Autonomous vehicles (AVs), as one of the cores in future intelligent transportation systems (ITSs), can facilitate reliable and safe traffic operations and services. The ability to automatically perform effective AV motion planning and deploy efficient perception systems is vital for advancing the quality of core transportation services. However, existing research studies have only considered the applications of either of these approaches, which neglect their necessary interactions in real-world AV motion planning systems. To address this problem, we design an AV motion planning strategy based on motion prediction and V2V communication. Specifically, we propose the perception system and V2V communication module to provide real-time traffic and vehicular information to the participated AVs. Then, we formulate the AV lane-change motion planning problem through the scope of model predictive control based problem, as well as proposing the method on learning optimal motion planning by means of a novel deep learning technique. We conduct extensive case studies to evaluate the performance of the proposed system model. Our experimental results demonstrate the effectiveness of the proposed system model under various traffic conditions. In addition, the robustness of the perception system is guaranteed by utilizing the Car Learning to Act (CARLA) system with available V2V communication. [ABSTRACT FROM AUTHOR]

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In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.

Robot-assisted walking has become a popular research field for helping mobility-limited people to walk more easily. Different from other walking-aid devices (e.g. exoskeletons and prosthesis), intelligent mobile walking-aids (IMWs) are invented for helping the visually impaired or people in need (e.g. the elderly) to walk in daily life. This paper reviews various related literatures on IMWs and dwells on three kinds of perception systems and perception algorithms of IMWs to explain how IMWs understand the user's motion states or tendency. Besides, the control strategies of IMWs under the normal case concerned are classified and compared. The safety measures for preventing the user from abnormal cases (e.g. encountering obstacles, the user's stumbling and falling, faults of IMWs) are introduced in detail as well. The performance of current safety measures for the user's fall detection and prevention has been evaluated and concluded. At the end of the article, the discussion and perspective of IMWs are presented. [ABSTRACT FROM AUTHOR]

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models' tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

Mobile Internet allows users to connect to the Internet anytime and anywhere. It has penetrated into every corner of human social life, and has had a tremendous impact, which has aroused great attention from all over the world. This paper introduces the concept of mobile Internet architecture, reference model and basic knowledge of technical characteristics; and then expounds the development status of key technologies of mobile Internet, combining the development trend of the world of information and network technology, the mobile Internet technology development are forecasted. Mobile Internet is a combination of mobile communication and traditional Internet which is the IT field at present and in the future for a long period of time. The rapid growth of data show that the global mobile Internet is still in the primary stage, there is still many problems to be solved and is not clear. This paper first introduces the basic concept of mobile Internet, including the definition, function the characteristics and architecture; basic research system given in the mobile Internet, discusses its components, including mobile terminals, access network, application service and the security and privacy aspects of the research status, existing problems and solutions. Finally, we discuss the research and development trend of mobile Internet in the future. The experimental results validated the performance of the proposed method, it outperforms compared with the other state-of-the-art models. [ABSTRACT FROM AUTHOR]

This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.