An important task for progress in IVHS (Intelligent Vehicle Highway Systems) is the development of methods for real-time traffic scene analysis. All three major applications of IVHS - ADIS (Advanced Driver Information Systems), ATMS (Advanced Traffic Management Systems), and AVCS (Automated Vehicle Control Systems) - could benefit from accurate, high-level descriptions of traffic situations. For example, an ADIS and an ATMS could use information about traffic congestion and accidents to alert drivers or to direct vehicles to alternate routes. An ATMS also could analyze local traffic at intersections to identify those with higher risk of accidents. Finally, an AVCS would need information about the actions of neighboring vehicles and the condition of traffic lanes ahead to control an automated car moving along a freeway .
End-to-end learning has recently emerged as a promising technique to tackle the problem of autonomous driving. Existing works show that learning a navigation policy from raw sensor data may reduce the system's reliance on external sensing systems, (e.g. GPS), and/or outperform traditional methods based on state estimation and planning. However, existing end-to-end methods generally trade off performance for safety, hindering their diffusion to real-life applications. For example, when confronted with an input which is radically different from the training data, end-to-end autonomous driving systems are likely to fail, compromising the safety of the vehicle. To detect such failure cases, this work proposes a general framework for uncertainty estimation which enables a policy trained end-to-end to predict not only action commands, but also a confidence about its own predictions. In contrast to previous works, our framework can be applied to any existing neural network and task, without the need to change the network's architecture or loss, or to train the network. In order to do so, we generate confidence levels by forward propagation of input and model uncertainties using Bayesian inference. We test our framework on the task of steering angle regression for an autonomous car, and compare our approach to existing methods with both qualitative and quantitative results on a real dataset. Finally, we show an interesting by-product of our framework: robustness against adversarial attacks.
Damm, Werner (Carl von Ossietzky Universität Oldenburg) | Fränzle, Martin (Carl von Ossietzky Universität Oldenburg) | Gerwinn, Sebastian (OFFIS e. V.) | Kröger, Paul (Carl von Ossietzky Universität Oldenburg)
Algorithms incorporating learned functionality play an increasingly important role for highly automated vehicles. Their impressive performance within environmental perception and other tasks central to automated driving comes at the price of a hitherto unsolved functional verification problem within safety analysis. We propose to combine statistical guarantee statements about the generalisation ability of learning algorithms with the functional architecture as well as constraints about the dynamics and ontology of the physical world, yielding an integrated formulation of the safety verification problem of functional architectures comprising artificial intelligence components. Its formulation as a probabilistic constraint system enables calculation of low risk manoeuvres. We illustrate the proposed scheme on a simple automotive scenario featuring unreliable environmental perception.
Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances of demonstrating system safety. This work frames the partially observable and belief-dependent problem of test scheduling as a Markov decision process, which can be solved efficiently to yield closed-loop manufacturer testing policies. By solving for policies over a wide range of problem formulations, we are able to provide high-level guidance for manufacturers and regulators on issues relating to the testing of safety-critical systems. This guidance spans an array of topics, including circumstances under which manufacturers should continue testing despite observed incidents, when manufacturers should test aggressively, and when regulators should increase or reduce the real-world testing requirements for an autonomous vehicle.
General intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow intelligence, the ability to solve a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial General Intelligence (AGI) presents dangers that narrow intelligence does not: if something smarter than us across every domain were indifferent to our concerns, it would be an existential threat to humanity, just as we threaten many species despite no ill will. Even the theory of how to maintain the alignment of an AGI's goals with our own has proven highly elusive. We present the first algorithm we are aware of for asymptotically unambitious AGI, where "unambitiousness" includes not seeking arbitrary power. Thus, we identify an exception to the Instrumental Convergence Thesis, which is roughly that by default, an AGI would seek power, including over us.