We come across many cyber-physical systems in our daily lives. The computer that controls a washing machine is one example\u2014it uses inputs, like the type of clothing you are washing or the water temperature you\u2019d like to use, and has sensors that activate water valves and heating elements. The computer directs what the machine does with your clothing. But Mancuso and the members of his Cyber-Physical Systems Lab, in BU\u2019s Center for Computing & Data Sciences, aren\u2019t interested in washing machines; they are focused on safety-critical<\/em> cyber-physical systems\u2014for example, the systems that control planes and cars, both manned and autonomous\u2014and ensure they operate safely and efficiently.<\/p>\n \u201cEven a regular non-self-driving car is pretty much a computer on wheels\u2014and we entrust our lives to them,\u201d Mancuso says. All of the projects in his lab are aimed at developing safer, more efficient, and more cost-effective cyber-physical systems. \u201cNASA can afford to spend billions of dollars to create a rover and custom build their [computer] chips to make sure that rover can survive 100 sols on Planet X,\u201d explains Mancuso. \u201cBut if we\u2019re talking about bringing safe, autonomous driving to the masses, we\u2019ll want to do that with commercial systems off the shelf. My approaches are targeting this kind of application where we want high-assurance cyber-physical systems that can be built without spending billions of dollars.\u201d<\/p>\n Recent headlines show the perils of safety-critical system failures. A Norfolk Southern freight train transporting toxic chemicals derailed in East Palestine, Ohio, in February 2023, causing a fire and a chemical spill. The National Transportation Safety Board\u2019s preliminary report on the derailment found that crew members weren\u2019t alerted to an overheated wheel bearing quickly enough. Then, in March 2023, another Norfolk Southern train derailed outside Springfield, Ohio.<\/p>\n Autonomous cars have also had their share of incidents. In June 2023, the Washington Post<\/em> reported<\/a> that, according to National Highway Traffic Safety Administration data, Teslas in \u201cautopilot mode\u201d were involved in more than 700 crashes, including 17 fatal accidents, since 2019. The article cited one accident that occurred in North Carolina in March 2023, in which a Tesla allegedly in \u201cautopilot mode\u201d and going 45 mph failed to brake, striking and injuring a teen getting off a school bus.<\/p>\n These incidents emphasize the importance of a key focus of Mancuso\u2019s research: timeliness. Take a car that is supposed to sense an obstacle and apply braking. \u201cIn testing that, it\u2019s not enough that it detects that there is a kid-size mannequin on the road. It also has to be sure to brake on time,\u201d says Mancuso. Airbag deployment is another example of the importance of timing. If it deploys too late, then there is a risk of the driver hitting the steering wheel; too early, and the airbag is already partially deflated by the time the driver lands on it.<\/p>\n A regular non-self-driving car is pretty much a computer on wheels\u2014and we entrust our lives to them.<\/p>\n Much of the research in Mancuso\u2019s lab is concerned with narrowing the right window of time for an action to be performed. \u201cOur mission is: Can we guarantee that strong temporal requirements can be met in spite of the increasing complexity of both the software and the hardware in platforms?\u201d he says.<\/p>\n To accomplish that, Mancuso and his students work at the intersection of software and hardware, attempting to improve a system\u2019s context awareness (knowing what\u2019s going on in the environment around it) and self-awareness (knowing what is going on inside itself) without sacrificing, and even improving, timeliness.<\/p>\nTime Is of the Essence<\/h2>\n
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