Browse Topic: Level 1 (Driver assistance)
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
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
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
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