Itisworth noting that there exist possible scenarios fortheabove inequalities toholdandtherefore achievingtheworst-case error. A.3.2 All-PerturbedBound In the following proof for Theorem 2, we apply similar steps in Appendix A.2 and consider the difference between set of pairwise margin under natural and weight perturbation setting, recall in Theorem2wedefinedthat fW(x)=WL(..WN...ρ(W1x)...)andfcW(x)= ˆW We note that each convolution operation can be described as matrix multiplication of a doubly block Toeplitz matrix. Inequality(d)resultsfromusingtriangle inequality and taking its maximum, inequality (e) isby the definition of margin and inequality (f) comes fromthefactthatwithReLU wehavekρ(Ax)k1 kAxk1. Wedenote theloss function asCE(), and during training, hard label was applied. Once we have calculated an upper bound forR(ˆ`F), then Theorem 4 is a direct consequence of Lemma2and3.

Advanced Driver Assistance Systems (ADAS) alert drivers during safety-critical scenarios but often provide superfluous alerts due to a lack of consideration for drivers' knowledge or scene awareness. Modeling these aspects together in a data-driven way is challenging due to the scarcity of critical scenario data with in-cabin driver state and world state recorded together. We explore the benefits of driver modeling in the context of Forward Collision Warning (FCW) systems. Working with real-world video dataset of on-road FCW deployments, we collect observers' subjective validity rating of the deployed alerts. We also annotate participants' gaze-to-objects and extract 3D trajectories of the ego vehicle and other vehicles semi-automatically. We generate a risk estimate of the scene and the drivers' perception in a two step process: First, we model the movement of vehicles in a given scenario as a joint trajectory forecasting problem. Then, we reason about the drivers' risk perception of the scene by counterfactually modifying the input to the forecasting model to represent the drivers' actual observations of vehicles in the scene. The difference in these behaviours gives us an estimate of driver behaviour that accounts for their actual (inattentive) observations and their downstream effect on overall scene risk. We compare both a learned scene representation as well as a more traditional ``worse-case'' deceleration model to achieve the future trajectory forecast. Our experiments show that using this risk formulation to generate FCW alerts may lead to improved false positive rate of FCWs and improved FCW timing.

Nauto is a leading provider of advanced driver assistance systems that improve the safety of commercial fleets today and the autonomous vehicles of tomorrow. To that end, we process terabytes of driving data a month, collected by windshield-mounted devices from vehicles around the world. This data is used to continuously improve the models that power our vehicle safety stack, from the real-time predictive collision alerts deployed to our devices, to the safety analytics that run on the cloud. Beyond providing immediate safety value to the drivers, our features play the important role of shaping their own evolution. If we want to improve the vehicle detection powering Forward Collision Warning (FCW), the first place we will look is the false positives triggered by FCW.

The leaders of the Senate Homeland Security and Government Affairs Committee are looking to get the federal workforce – particularly program managers and acquisition specialists – on board with artificial intelligence. A new bill, the Artificial Intelligence Training for the Acquisition Workforce Act, would set up a training program for federal workers to learn more about AI technology, from its scientific underpinnings to risks associated with its use. "Federal employees must be aware of the ethical implications, risks, and benefits associated with AI," Sen. Gary Peters (D-Mich.), the chair of the committee and a co-sponsor of the bill, said in a statement. "This important legislation will help protect our national security, help us remain competitive in the long run, and make sure AI technology is used properly." The bill tasks the Office of Management and Budget with establishing training to help the federal workforce understand the science behind AI, how AI can benefit government programs as well as the risks associated with AI, including privacy violations and inherent bias in algorithms that power AI programs.

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. Our attack goal is to negatively affect human braking decisions by causing KF to output incorrect state estimations that lead to false or delayed alerts. We accomplish this by sequentially manipulating measure ments fed into the KF, and propose a novel Model Predictive Control (MPC) approach to compute the optimal manipulation. Via experiments conducted in a simulated driving environment, we show that the attacker is able to successfully change FCW alert signals through planned manipulation over measurements prior to the desired target time. These results demonstrate that our attack can stealthily mislead a distracted human driver and cause vehicle collisions.

Investments in cybersecurity and artificial intelligence efforts will likely continue to increase as overall defense spending remains flat in future years, but a worsening pandemic could dampen those projections, according to new analysis from the Professional Services Council's latest research on federal budgets. The Defense Department is largely expected to keep pace with current budget levels, potentially seeing very modest 2% growth to topline budgets, PSC's report projects. That trend could also extend to IT modernization efforts. Senate Appropriators weighed in today on 2021 spending, proposing a $696 billion defense budget, slightly above 2020 levels and slightly below the Trump administration's funding request. The House passed their funding bill in July at $694.6 billion.

The Defense Department is focused on implementing its ethics principles for artificial intelligence, especially when it comes to health-related data. But tech experts warn against conflating ethics as just another compliance checklist. Jane Pinelis, who leads test, evaluation, and assessment for the DOD's Joint Artificial Intelligence Center, said preserving personal health information is one of the JAIC's biggest priorities. "On the health side, one of the biggest things that we're concerned about is the preservation of personal health information," Pinelis said during an Oct. 22 Defense One NextGov event on AI. "On something else, we might be worried about equitability and bias, how do we train these models, what kind of data do we use in training them, and what does that mean about future applications." The JAIC announced progress with its Predictive Health effort on Oct. 21, which aims to reduce the time it takes to diagnose cancer.

In the remote work environment spawned by the COVID-19 pandemic, more flexible, quicker methods of getting systems the authority to securely operate is more critical than ever, said a top IT advisor at the Department of Health and Human Services. "Machine learning is critical in terms of fighting fire with fire. You're going to lose that battle" with hackers, said Oki Mek, senior advisor to the agency's CIO and its ReImagine project. HHS is one of the agencies at the center of the federal government's response to the COVID pandemic. The agency is "getting hit hard" by hackers attempting to penetrate its networks, said Mek.

The Pentagon's Joint Artificial Intelligence Center is focused on overlaying artificial intelligence tools on the military's mega information-sharing platform effort, called Joint All Domain Command and Control. Nand Mulchandani, JAIC's acting director, told reporters during a July 8 news briefing the center is "spending a lot of time and resources focused on building the AI components on top of JADC2," which is a patchwork quilt of platforms to improve coordination and information sharing. This involves figuring out how to build AI components, such as data, AI modeling, training and deployment, across all domains including cyber, he said. Mulchandani said JAIC is also investing in cognitive assistance technologies, helping human operators make better decisions, using "predictive analytics or picking out particular things of interest, and those types of information overload cleanup." Working through objections to the Defense Department's use of AI in weapons systems is still a chief concern, however.

The Pentagon's Joint Artificial Intelligence Center is focused on overlaying artificial intelligence tools on the military's mega information-sharing platform effort, called Joint All Domain Command and Control. Nand Mulchandani, JAIC's acting director, told reporters during a July 8 news briefing the center is "spending a lot of time and resources focused on building the AI components on top of JADC2," which is a patchwork quilt of platforms to improve coordination and information sharing. This involves figuring out how to build AI components, such as data, AI modeling, training and deployment, across all domains including cyber, he said. Mulchandani said JAIC is also investing in cognitive assistance technologies, helping human operators make better decisions, using "predictive analytics or picking out particular things of interest, and those types of information overload cleanup." Working through objections to the Defense Department's use of AI in weapons systems is still a chief concern, however.