Browse Topic: Automation
Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications
As the autonomy of ADAS features are moving from SAE level 0 autonomy to SAE level 5 autonomy of operation, reliance on AI/ML based algorithms in ADAS critical functions like perception, fusion and path planning are increasing predominantly. AI/ML based algorithms offer exceptional performance of the ADAS features, at the same time these advanced algorithms also bring in safety challenges as well. This paper explores the functional safety aspects of AI/ML based systems in ADAS functions like perception, object fusion and path planning, by discussing the safety requirements development for AI/ML systems, dataset safety life cycle, verification and validation of AI systems, and safety analysis used for AI systems. Among all the safety aspects listed above, emphasis is put on dataset safety lifecycle as that is not only the most important element for training ML based algorithms for ADAS usage, but also the most cumbersome and expensive. The safety characteristics associated with dataset
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain plant features a wetted area of 600 square meters and a sprinkled rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of Radar, Lidar, and Camera
Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain
The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing
This SAE Edge Research Report explores advancements in next-generation mobility, focusing on digitalized and smart cockpits and cabins. It offers literature review, examining current customer experiences with traditional vehicles and future mobility expectations. Key topics include integrating smart cockpit and cabin technologies, addressing challenges in customer and user experience (UX) in digital environments, and discussing strategies for transitioning from traditional vehicles to electric ones while educating customers. User Experience for Digitalized and Smart Cockpits and Cabins of Next-gen Mobility covers both on- and off-vehicle experiences, analyzing complexities in developing and deploying digital products and services with effective user interfaces. Emphasis is placed on meeting UX requirements, gaining user acceptance, and avoiding trust issues due to poor UX. Additionally, the report concludes with suggestions for improving UX in digital products and services for future
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful
Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication
This SAE Recommended Practice establishes a uniform, powered vehicle test procedure and minimum performance requirement for lane departure warning systems used in highway trucks and buses greater than 4546 kg (10000 pounds) gross vehicle weight (GVW). Systems similar in function but different in scope and complexity, including lane keeping/lane assist and merge assist, are not included in this document. This document does not apply to trailers, dollies, etc. This document does not intend to exclude any particular system or sensor technology. This document will test the functionality of the lane departure warning system (LDWS) (e.g., ability to detect lane presence and ability to detect an unintended lane departure), its ability to indicate LDWS engagement, its ability to indicate LDWS disengagement, and its ability to determine the point at which the LDWS notifies the human machine interface (HMI) or vehicle control system that a lane departure event is detected. Moreover, this
Nowadays, there are many technologies emerging like firefighting robots, quadcopters, and drones which are capable of operating in hazardous disaster scenarios. In recent years, fire emergencies have become an increasingly serious problem, leading to hundreds of deaths, thousands of injuries, and the destruction of property worth millions of dollars. According to the National Crime Records Bureau (NCRB), India recorded approximately 1,218 fire incidents resulting in 1,694 deaths in 2020 alone. Globally, the World Health Organization (WHO) estimates that fires account for around 265,000 deaths each year, with the majority occurring in low- and middle-income countries. The existing fire-extinguishing systems are often inefficient and lack proper testing, causing significant delays in firefighting efforts. These delays become even more critical in situations involving high-rise buildings or bushfires, where reaching the affected areas is particularly challenging. The leading causes of
This article provides a comprehensive review of existing literature on AI-based functions and verification methods within vehicular systems. Initially, the introduction of these AI-based functions in these systems is outlined. Subsequently, the focus shifts to synthetic environments and their pivotal role in the verification process of AI-based vehicle functions. The algorithms used within the AI-based functions focus primarily on the paradigm of deep learning. We investigate the constituent components of these synthetic environments and the intricate relationships with vehicle systems in the verification and validation domain of the system. In the following, alternative approaches are discussed, serving as complementary methods for verification without direct involvement in synthetic environment development. These approaches include data-oriented methodologies employing statistical techniques and AI-centric strategies focusing solely on the core deep learning algorithm.
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