Browse Topic: Level 1 (Driver assistance)
1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential relationships and influences during the design phase. The novelty of this framework comes from providing a holistic evaluation that captures testing of interactions between modules in addition to component-level testing, while other frameworks focus on testing individual component behaviors. It also assesses the equality of two interactions, meaning it ensures that two interactions behave the same way for consistent results. Moreover, the framework serves as a valuable tool for AV designers and safety regulators to aid in establishing robust design and assessment approaches. This work highlights the need for a common framework to thoroughly test AVs and gain a holistic understanding of system interactions. Finally, the framework aims to understand how to mitigate potential influences leading to AV malfunctions to advance the development and deployment of safe and reliable Automated Vehicles. The work focuses on level 1 and level 4 automated driving features to simplify the work, although it can be from level 1 to level 5. Although framework performance is inherently difficult to quantify, this framework’s performance can be reflected through its ability to accurately capture system interactions for improved AV design and support a broader usability among AV stakeholders. In the future, the framework can be expanded to include additional elements, such as infrastructure or other vehicles, to analyze information provided to AVs, allowing experts from various domains to collaborate, create similar models, integrate them when feasible, and model the interactions in real-time.
One chip, multiple benefits. That's the claim made by U.S. semiconductor company Qualcomm Technologies Inc. about its new, scalable system-on-a-chip (SoC) product family, called Snapdragon Ride Flex. Unveiled at CES2023 and due to enter the market in early 2024, Snapdragon Flex is the auto industry's first scalable family of SoCs that can run a digital cockpit and ADAS features simultaneously, according to the company. Snapdragon Ride Flex is the latest member of the Snapdragon SoC family. Qualcomm's first-generation Ride Platforms are currently available in commercialized vehicles. Newer generations, which include the Ride Vision stack that can handle ADAS applications, are being tested by Tier 1s. They are expected to arrive on MY2025 vehicles from various OEMs, according to Qualcomm.
ABSTRACT The transportation industry annually travels more than 6 times as many miles as passenger vehicles [1]. The fuel cost associated with this represents 38% of the total marginal operating cost for this industry [8]. As a result, industry’s interest in applications of autonomy have grown. One application of this technology is Cooperative Adaptive Cruise Control (CACC) using Dedicated Short-Range Communications (DSRC). Auburn University outfitted four class 8 vehicles, two Peterbilt 579’s and two M915’s, with a basic hardware suite, and software library to enable level 1 autonomy. These algorithms were tested in controlled environments, such as the American Center for Mobility (ACM), and on public roads, such as highway 280 in Alabama, and Interstates 275/696 in Michigan. This paper reviews the results of these real-world tests and discusses the anomalies and failures that occurred during testing. Citation: Jacob Ward, Patrick Smith, Dan Pierce, David Bevly, Paul Richardson, Sridhar Lakshmanan, Athanasios Argyris, Brandon Smyth, Cristian Adam, Scott Heim “Cooperative Adaptive Cruise Control (CACC) in Controlled and Real-World Environments: Testing and Results”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.
Automated vehicle technology is rapidly increasing in capability and the adoption of these technologies will become more widespread in the future. In the intermediate stages of automation where the driver is required to supplement the automated technology, it may be necessary to evaluate the driver’s readiness to take-over a part or of all the dynamic driving task (SAE, 2016). Specifically, while driving with a level 2 or 3 automated driving feature, a challenge may be that drivers with low readiness fail to take over in an appropriate manner. One important implication of assessing driver readiness is to assess driver state. In this study, we investigated candidate for a driver readiness index which was compared between manual driving (Level 0) and ACC driving (Level 1). Additionally, one more method to evaluate the readiness of the driver is to measure whether the driver anticipates potential hazards (i.e., does their foot hover over the brake or throttle). To encourage this type of behavior, vehicles could include a human-machine interface (HMI) that supports the driver to understand where potential hazards exist; however, this would need to be designed to prevent annoyance. The hypothesis for the series of studies was that showing overall traffic status allows the driver to more rapidly prepare for potential hazards when compared with no additional information (i.e., next lane vehicle turn signal). This part of the current study measured driver behavior and traffic data along a designated route in a naturalistic setting. Several traffic scenarios were identified that include overt anticipatory behavior. In this paper, we had two research questions. One is how to measure driver readiness level. Two is what type of information would be useful for maintaining readiness.
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