Browse Topic: Automated driving systems
To establish and validate new systems incorporated into next generation vehicles, it is important to understand actual scenarios which the autonomous vehicles will likely encounter. Consequently, to do this, it is important to run Field Operational Tests (FOT). FOT is undertaken with many vehicles and large acquisition areas ensuing the capability and suitability of a continuous function, thus guaranteeing the randomization of test conditions. FOT and Use case(a software testing technique designed to ensure that the system under test meets and exceeds the stakeholders' expectations) scenario recordings capture is very expensive, due to the amount of necessary material (vehicles, measurement equipment/objectives, headcount, data storage capacity/complexity, trained drivers/professionals) and all-time robust working vehicle setup is not always available, moreover mileage is directly proportional to time, along with that it cannot be scaled up due to physical limitations. During the early
ABSTRACT Significant Design for Reliability (DfR) methodology challenges are created with the integration of autonomous vehicle technologies via applique systems in a ground military vehicle domain. Voice of the customer data indicates current passenger vehicle usage cycles are typically 5% or less (approximately 72 minutes of use in a twenty-four hour period) [2]. The time during which vehicles currently lay dormant due to drivers being otherwise occupied could change with autonomous vehicles. Within the context of the fully mature autonomous military vehicle environment, the daily vehicle usage rate could grow to 95% or more. Due to this potential increase in the duty or usage cycle of an autonomous military vehicle by an order of magnitude, several issues which impact reliability are worth exploring. Citation: M. Majcher, J. Wasiloff, “New Design for Reliability (DfR) Needs and Strategies for Emerging Autonomous Ground Vehicles”, In Proceedings of the Ground Vehicle Systems
ABSTRACT FEV North America will discuss application of advanced automotive cybersecurity to smart vehicle projects, - software safety - software architecture and how it applies to similar features and capabilities across the fleet of DoD combat and tactical vehicles. The analogous system architectures of automotive and military vehicles with advanced architectures, distributed electronic control units, connectivity to networks, user interfaces and maintenance networks and interface points clearly open an opportunity for DoD to leverage the technology techniques, hardware, software, management and human resources to drive implementation costs down while implementing fleet modifications, infrastructure methodology and many of the features of the automotive cyber security spectrum. Two of the primary automotive and DoD subsystems most relevant to Cyber Security threat and protection are the automotive connected vehicles analogous to the DoD Command, Control, Communications, Computers
ABSTRACT The IGVC offers a design experience that is at the very cutting edge of engineering education. It is multidisciplinary, theory-based, hands-on, team implemented, outcome assessed, and based on product realization. It encompasses the very latest technologies impacting industrial development and taps subjects of high interest to students. Design and construction of an Intelligent Vehicle fits well in a two semester senior year design capstone course, or an extracurricular activity earning design credit. The deadline of an end-of-term competition is a real-world constraint that includes the excitement of potential winning recognition and financial gain. Students at all levels of undergraduate and graduate education can contribute to the team effort, and those at the lower levels benefit greatly from the experience and mentoring of those at higher levels. Team organization and leadership are practiced, and there are even roles for team members from business and engineering
ABSTRACT Today we have autonomous vehicles already on select road-ways and regions of this country operating in and around humans and human operated vehicles. The companies developing and testing these systems have experienced varied degrees of success and failure with regard to safe operations within this public space. There have been safety incidents that have made national headlines (when human fatalities have occurred) and their also exist a litany of other physical incidents, usually with human operated systems, that have not grabbed the headlines. Some of the select communities where these autonomous systems have been operationally tested have revoked access to their roadways (kicked out) some of these companies. As a result of these incidents recent data suggests that the public trust in autonomous vehicles is eroding [1]. This situation is couponed by the fact that there are no established safety standards, measures or technological methods to help local, state or national
ABSTRACT The automotive and defense industries are going through a period of disruption with the advent of Connected and Automated Vehicles (CAV) driven primarily by innovations in affordable sensor technologies, drive-by-wire systems, and Artificial Intelligence-based decision support systems. One of the primary tools in the testing and validation of these systems is a comparison between virtual and physical-based simulations, which provides a low-cost, systems-approach testing of frequently occurring driving scenarios such as vehicle platooning and edge cases and sensor-spoofing in congested areas. Consequently, the project team developed a robotic vehicle platform—Scaled Testbed for Automated and Robotic Systems (STARS)—to be used for accelerated testing elements of Automated Driving Systems (ADS) including data acquisition through sensor-fusion practices typically observed in the field of robotics. This paper will highlight the implementation of STARS as a scaled testbed for rapid
ABSTRACT The IGVC offers a design experience that is at the very cutting edge of engineering education, with a particular focus in developing engineering control/sensor integration experience for the college student participants. A main challenge area for teams is the proper processing of all the vehicle sensor feeds, optimal integration of the sensor feeds into a world map and the vehicle leveraging that world map to plot a safe course using robust control algorithms. This has been an ongoing challenge throughout the 26 year history of the competition and is a challenge shared with the growing autonomous vehicle industry. High consistency, reliability and redundancy of sensor feeds, accurate sensor fusion and fault-tolerant vehicle controls are critical, as even small misinterpretations can cause catastrophic results, as evidenced by the recent serious vehicle crashes experienced by self-driving companies including Tesla and Uber Optimal control techniques & sensor selection
ABSTRACT Over time, the National Institute of Standards and Technology (NIST) has refined the 4Dimension / Real-time Control System (4D/RCS) architecture for use in Unmanned Ground Vehicles (UGVs). This architecture, when applied to a fully autonomous vehicle designed for missions in urban environments, can greatly assist in the process of saving time and lives by creating a more intelligent vehicle that acts in a safer and more efficient manner. Southwest Research Institute (SwRI®) has undertaken the Southwest Safe Transport Initiative (SSTI) aimed at investigating the development and commercialization of vehicle autonomy as well as vehicle-based telemetry systems to improve active safety systems and autonomy. This paper will discuss the implementation of the 4D/RCS architecture to the SSTI autonomous vehicle, a 2006 Ford Explorer
ABSTRACT The NAUS ATO (2004-2009) was a follow-on program to the Robotic Follower ATO (2000- 2004) and built on the concept of semi-autonomous leader follower technology to achieve dynamic robotic movement in tactical formations. The NAUS ATO also developed and tested an Unmanned Ground Vehicle (UGV) Self-Security system capable of detecting, tracking, and predicting the intent of human beings in the vicinity of the vehicle. The ATO concluded its Engineering and Evaluation Testing (EET) with a capstone demonstration in October 2008. This paper will detail the technology developed and utilized under the program as well as report on the EET results to the robotic community
ABSTRACT The IGVC offers a design experience that is at the very cutting edge of engineering education, with a particular focus in developing engineering control/sensor integration experience for the college student participants. A main challenge area for teams is the proper processing of all the vehicle sensor feeds, optimal integration of the sensor feeds into a world map and the vehicle leveraging that world map to plot a safe course using robust control algorithms. This has been an ongoing challenge throughout the 27 year history of the competition and is a challenge shared with the growing autonomous vehicle industry. High consistency, reliability and redundancy of sensor feeds, accurate sensor fusion and fault-tolerant vehicle controls are critical, as even small misinterpretations can cause catastrophic results, as evidenced by the recent serious vehicle crashes experienced by self-driving companies including Tesla and Uber Optimal control techniques & sensor selection
ABSTRACT The concept of Autonomous Vehicles ultimately generating an “order of magnitude” potential increase in the duty or usage cycle of a vehicle needs to be addressed in terms of impact on the reliability domain. Voice of the customer data indicates current passenger vehicle usage cycles are typically very low, 5% or less. Meaning, out of a 24 hour day, perhaps the average vehicle is actually driven only 70 minutes or less. Therefore, approximately 95% of the day, the vehicles lay dormant in an unused state. Within the context of future fully mature Autonomous Vehicle environment involving structured car sharing, the daily vehicle usage rate could grow to 95% or more
ABSTRACT Lockheed Martin Missiles and Fire Control has developed a robotic site shuttle for use in structured areas, such as commercial railroad yards, port operations and storage/distribution industries. The purpose of the site shuttle is to provide an autonomous taxi service for personnel needing to move to various locations around the facilities. Many rail yards, ports and storage area are very large, so “taxi” transportation is vital to maintain efficiency and safety. The shuttle vehicles operate in complete autonomy: they have no steering wheel, accelerator or brake pedal. Personnel using the vehicles have only emergency stop buttons in the front and rear of the vehicles. Once implemented, the robotic shuttles will considerably reduce the costs of operation for the company. This need is consistent throughout the rail, port and storage/distribution industries, as all need to move personnel around their yards
Ongoing research in simulated vehicle crash environments utilizes postmortem human subjects (PMHS) as the closest approximation to live human response. Lumbar spine injuries are common in vehicle crashes, necessitating accurate assessment methods of lumbar loads. This study evaluates the effectiveness of lumbar intervertebral disc (IVD) pressure sensors in detecting various loading conditions on component PMHS lumbar spines, aiming to develop a reliable insertion method and assess sensor performance under different loading scenarios. The pressure sensor insertion method development involved selecting a suitable sensor, using a customized needle-insertion technique, and precisely placing sensors into the center of lumbar IVDs. Computed tomography (CT) scans were utilized to determine insertion depth and location, ensuring minimal tissue disruption during sensor insertion. Tests were conducted on PMHS lumbar spines using a robotic test system for controlled loading in flexion
Southwest Research Institute has developed off-road autonomous driving tools with a focus on stealth for the military and agility for space and agriculture clients. The vision-based system pairs stereo cameras with novel algorithms, eliminating the need for LiDAR and active sensors
The deployment of autonomous urban buses brings with it the hope of addressing concerns associated with safety and aging drivers. However, issues related autonomous vehicle (AV) positioning and interactions with road users pose challenges to realizing these benefits. This report covers unsettled issues and potential solutions related to the operation of autonomous urban buses, including the crucial need for all-weather localization capabilities to ensure reliable navigation in diverse environmental conditions. Additionally, minimizing the gap between AVs and platforms during designated parking requires precise localization. Next-gen Urban Buses: Autonomy and Connectivity addresses the challenge of predicting the intentions of pedestrians, vehicles, and obstacles for appropriate responses, the detection of traffic police gestures to ensure compliance with traffic signals, and the optimization of traffic performance through urban platooning—including the need for advanced communication
Simulation company rFpro has already mapped over 180 digital locations around the world, including public roads, proving grounds and race circuits. But the company's latest is by far its biggest and most complicated. Matt Daley, technical director at rFpro, announced at AutoSens USA 2024 that its new Los Angeles route is an “absolutely massive, complicated model” of a 36-km (22-mile) loop that can be virtually driven in both directions. Along these digital roads - which were built off survey-grade LIDAR data with a 1 cm by 1 cm (1.1-in by 1.1 in) X-Y grid - rFpro has added over 12,000 buildings, 13,000 pieces of street infrastructure (like signs and lamps), and 40,000 pieces of vegetation. “It's a fantastic location,” Daley said. “It's a huge array of different types of challenging infrastructure for AVs. You can drive this loop with full vehicle dynamic inputs, ready to excite the suspension and, especially with AVs, shake the sensors in the correct way as you would be getting if you
Verification and validation (V&V) is the cornerstone of safety in the automotive industry. The V&V process ensures that every component in a vehicle functions according to its specifications. Automated driving functionality poses considerable challenges to the V&V process, especially when data-driven AI components are present in the system. The aim of this work is to outline a methodology for V&V of AI-based systems. The backbone of this methodology is bridging the semantic gap between the symbolic level at which the operational design domain and requirements are typically specified, and the sub-symbolic, statistical level at which data-driven AI components function. This is accomplished by combining a probabilistic model of the operational design domain and an FMEA of AI with a fitness-for-purpose model of the system itself. The fitness-for-purpose model allows for reasoning about the behavior of the system in its environment, which we argue is essential to determine whether the
To shape future mobility MAHLE has committed itself to foster wireless charging for electrical vehicles. The standardized wireless power transfer of 11 kW at a voltage level of 800 V significantly improves the end user experience for charging an electric vehicle without the need to handle a connector and cable anymore. Combined with automated parking and autonomous driving systems, the challenge to charge fleets without user interaction is solved. Wireless charging is based on inductive power transfer. In the ground assembly’s (GA) power transfer coil, a magnetic field is generated which induces a voltage in the vehicle assembly (VA) power transfer coil. To transfer the power from grid to battery with a high efficiency up to 92% the power transfer coils are compensated with resonant circuits. In this paper the Differential-Inductive-Positioning-System (DIPS) to align a vehicle on the GA for parking will be presented. This system utilizes five standardized magnetic fields which are
In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm, which is further enhanced through the integration of wheel encoder and altitude data. Based on the generated map a LiDAR localization algorithm is used to determine the pose of the vehicle. The FAST-LIO based localization algorithm is enhanced by integrating relative LiDAR Odometry estimates and by using a simple yet effective delay compensation method to
The conventional process of last-mile delivery logistics often leads to safety problems for road users and a high level of environmental pollution. Delivery drivers must deal with frequent stops, search for a convenient parking spot and sometimes navigate through the narrow streets causing traffic congestion and possibly safety issues for the ego vehicle as well as for other traffic participants. This process is not only time consuming but also environmentally impactful, especially in low-emission zones where prolonged vehicle idling can lead to air pollution and to high operational costs. To overcome these challenges, a reliable system is required that not only ensures the flexible, safe and smooth delivery of goods but also cuts the costs and meets the delivery target. In the dynamic landscape of last-mile delivery, LogiSmile, an EU project, introduced a solution to urban delivery challenges through an innovative cooperation between an Autonomous Hub Vehicle (AHV) and an Autonomous
Artificial intelligence (AI)-based solutions are slowly making their way into mobile devices and other parts of our lives on a daily basis. By integrating AI into vehicles, many manufacturers are looking forward to developing autonomous cars. However, as of today, no existing autonomous vehicles (AVs) that are consumer ready have reached SAE Level 5 automation. To develop a consumer-ready AV, numerous problems need to be addressed. In this chapter we present a few of these unaddressed issues related to human-machine interaction design. They include interface implementation, speech interaction, emotion regulation, emotion detection, and driver trust. For each of these aspects, we present the subject in detail—including the area’s current state of research and development, its current challenges, and proposed solutions worth exploring
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