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Autonomous ships are expected to extensively rely on perception sensors for situation awareness and safety during challenging operations, such as reactive collision avoidance. However, sensor noise is inevitable and its impact on end-to-end decision-making has not been addressed yet. This study aims to develop a methodology to enhance the robustness of decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels. A Gaussian-based noisy perception sensor is employed, where its noisy measurements and noise variance are incorporated into the decision-making as observations. A deep reinforcement learning agent is employed, which is trained in different noise variances. Robustness metrics that quantify the robustness of the agent's decision-making are defined. A case study of a container ship using a LIDAR in a single static obstacle environment is investigated. Simulation results indicate sophisticated decision-making of the trained agent prioritising safety over efficiency when the noise variance is higher by conducting larger evasive manoeuvres. Sensitivity analysis indicates the criticality of the noise variance observation on the agent's decision-making. Robustness is verified against noise variance up to 132% from its maximum trained value. Robustness is verified only up to 76% when the agent is trained without the noise variance observation with lack of its prior sophisticated decision-making. This study contributes towards the development of autonomous systems that can make safe and robust decisions under uncertainty. [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.

New advanced driver assistance system/automated driving (ADAS/AD) functions have the potential to significantly enhance the safety of vehicle passengers and road users, while also enabling new transportation applications and potentially reducing CO2 emissions. To achieve the next level of driving automation, i.e., SAE Level-3, physical test drives need to be supplemented by simulations in virtual test environments. A major challenge for today’s virtual test environments is to provide a realistic representation of the vehicle’s perception system (camera, lidar, radar). Therefore, new and improved sensor models are required to perform representative virtual tests that can supplement physical test drives. In this article, we present a computationally efficient, mathematically complete, and geometrically exact generic sensor modeling approach that solves the FOV (field of view) and occlusion task. We also discuss potential extensions, such as bounding-box cropping and sensor-specific, weather-dependent FOV-reduction approaches for camera, lidar, and radar. The performance of the new modeling approach is demonstrated using camera measurements from a test campaign conducted in Hungary in 2020 plus three artificial scenarios (a multi-target scenario with an adjacent truck occluding other road users and two traffic jam situations in which the ego vehicle is either a car or a truck). These scenarios are benchmarked against existing sensor modeling approaches that only exclude objects that are outside the sensor’s maximum detection range or angle. The modeling approach presented can be used as is or provide the basis for a more complex sensor model, as it reduces the number of potentially detectable targets and therefore improves the performance of subsequent simulation steps.

Application of Vehicular Ad Hoc Networks (VANETs) aims to help in the solution of some problems that have arisen in road transportation systems via short-range, low-latency mobile communication. The application of V2X (Vehicle-to-Everything) communication technologies to the next generation of Advanced Driver Assistance Systems (ADAS) is essential to the extension of the operational design domain (ODD) of the systems to provide safe, secure, and efficient automated driving solutions. Due to the safety-critical nature of the problem, the large-scale testing of V2X enabled ADAS solutions to evaluate and measure the anticipated quality and functionality of the experimental system is of great significance. This article proposes a novel ADAS application prototyping framework, using declarative programming, built on top of the popular Artery/OMNeT++ simulator. The framework is capable of simulating V2X-enabled ADAS applications using accurate network simulation and realistic simulated traffic on real-world maps. The solution features XML descriptions for application specification. The sensor model of Artery is used to provide information to applications. By using the simulator, one can conclude the performance of the applications and discover locations, circumstances and design patterns, where design limits should apply. [ABSTRACT FROM AUTHOR]

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving/driver assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, novel applications and standards in real world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support, computation algorithms and current development state. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about current regulations and market state of ADAS in China are reviewed in this paper, and other open issues related to ADAS are also introduced in particular. [ABSTRACT FROM AUTHOR]

Despite the relevance of autonomous vehicle driving algorithms, behavior tracking and virtual data modeling tools, and image recognition and urban sensing technologies, only limited research has been conducted on this topic. In this article, I cumulate previous research findings indicating that simulation and virtualization technologies, real-time image processing tools, and autonomous vehicle perception sensors curtail crash recurrence. I contribute to the literature on deep learning object detection and autonomous driving technologies, Internet of Things connected devices and sensing infrastructures, and visual recognition tools by showing that autonomous driving algorithms harness data-driven planning and deep learning technologies, digital mapping tools, and mobile wireless sensor networks. Throughout May 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “artificial moral agents” + “big data-driven transportation systems” + “autonomous vehicle perception sensors,” “virtual simulation algorithms,” and “geospatial mapping tools.” As I inspected research published in 2022, only 184 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 33, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT. [ABSTRACT FROM AUTHOR]

Application of Vehicular Ad Hoc Networks (VANETs) aims to help in the solution of some problems that have arisen in road transportation systems via short-range, low-latency mobile communication. The application of V2X (Vehicle-to-Everything) communication technologies to the next generation of Advanced Driver Assistance Systems (ADAS) is essential to the extension of the operational design domain (ODD) of the systems to provide safe, secure, and efficient automated driving solutions. Due to the safety-critical nature of the problem, the large-scale testing of V2X enabled ADAS solutions to evaluate and measure the anticipated quality and functionality of the experimental system is of great significance. This article proposes a novel ADAS application prototyping framework, using declarative programming, built on top of the popular Artery/OMNeT++ simulator. The framework is capable of simulating V2X-enabled ADAS applications using accurate network simulation and realistic simulated traffic on real-world maps. The solution features XML descriptions for application specification. The sensor model of Artery is used to provide information to applications. By using the simulator, one can conclude the performance of the applications and discover locations, circumstances and design patterns, where design limits should apply.

Sensor and perception simulation is key for simulation based testing of automated driving functions. Depending on the testing use-case, different cause-effect chains for the specific sensor technology become relevant and the demanded computation times differ. In this work, a novel approach for object-based lidar simulation is introduced, identifying and modeling major sensor effects while balancing effect fidelity and computation time. With an explicitly designed static experiment, simple object based models are falsified, showing the need of a novel approach. Therefore, refined bounding boxes are designed and integrated into occlusion calculation. This new approach is compared to an advanced ray tracing simulation on an object output level. The comparison is conducted on the cause-effect chain of partial object occlusion, which has been identified as highly relevant. The modeling approach challenges ray tracing as the magic bullet for high-fidelity lidar front-end simulation. In direct comparison with the ray tracing approach, our novel approach stands out due to its significantly lower computation time. The newly developed object-based model is open source and publicly available at https://gitlab.com/tuda-fzd/perception-sensor-modeling/object-based-generic-perception-object-model. [ABSTRACT FROM AUTHOR]

The global Connected and Autonomous Mobility industry is growing at a rapid pace. To ensure the successful adoption of connected automated mobility solutions, their safety, reliability and hence the public acceptance are paramount. It is widely known that in order to demonstrate that L3+ automated systems are safer with respect to human drivers, upwards of several millions of miles need to be driven. The only way to efficiently achieve this amount of tests in a timely manner is by using simulations and high fidelity virtual environments. Two key components of being able to test an automated system in a synthetic environment are validated sensor models and noise models for each sensor technology. In fact, the sensors are the element feeding information into the system in order to enable it to safely plan the trajectory and navigate. In this paper, we propose an innovative real-time LiDAR sensor model based on beam propagation and a probabilistic rain model, taking into account raindrop distribution and size. The model can seamlessly run in real-time, synchronised with the visual rendering, in immersive driving simulators, such as the WMG 3xD simulator. The models are developed using Unreal engine, therefore demonstrating that gaming technology can be merged with the Automated Vehicles (AVs) simulation toolchain for the creation and visualization of high fidelity scenarios and for AV accurate testing. This work can be extended to add more sensors and more noise factors or cyberattacks in real-time simulations. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) can detect and communicate with cooperative obstacles through established protocols. However, non-cooperative obstacles pose a significant threat to UAVs during low-flight operations. These obstacles include static obstacles like buildings, trees, or communication towers and dynamic objects like other UAVs. The application of autonomous UAVs in low-altitude surveillance has motivated research into non-cooperative local obstacle avoidance. This paper provides an overview of such solutions that have been proposed within the last decade. Unlike most literature that limits obstacle avoidance to algorithms, this work provides an in-depth review of obstacle avoidance components, namely the perception sensor, techniques, and hardware architecture of the obstacle avoidance system. This review categorizes the non-cooperative obstacle avoidance techniques into four groups: gap-based methods, geometric methods, repulsive force-based methods, and Artificial Intelligence (AI) based methods. This paper provides a comprehensive resource for researchers working on collision-free surveillance by autonomous UAVs at low altitudes. [ABSTRACT FROM AUTHOR]

Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery.

Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery. [ABSTRACT FROM AUTHOR]