Browse Topic: Level 0 (No driving automation)

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Safe Deployment of AI and ML Based Software and Algorithms in ADAS Systems2025-01-807104/01/2025
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
Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua, Chandrasekaran, Mukund
Technical Paper
System Engineering of an Advanced Driver Assistance System2019-01-087604/02/2019
Current Advanced Driver Assistance Systems (ADAS) often interact with the driver; aiding with either warnings or direct intervention. This work explores the development of an ADAS system to provide lane departure warning, forward collision warning, and a recommended following distance for a custom plug-in hybrid-electric vehicle. The system utilizes off-the-shelf hardware with in-house computer vision and sensor fusion algorithms to create a low-cost SAE Level 0 driver assistance system. The system utilizes a radar sensor as well as a camera to detect, classify, and track target vehicles. This work will illustrate the systems engineering methods used for outlining customer requirements, technical requirements, component selection, software development, simulation, vehicle fitment, and validation. Similar system engineering processes could be implemented for higher level SAE systems.
Stoddart, Evan, Chebolu, Subash, Midlam-Mohler, Shawn
Technical Paper
Identifying Traffic Scenarios to Evaluate Driver Readiness in Automated Driving: An Exploratory Study2018-01-050204/03/2018
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
Fukui, Toshinao, Remtema, Todd, Austin, Benjamin, Domeyer, Joshua
Technical Paper
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