A Unified Approach for Static and Runtime Verification: Framework and Applications

Static verification of software is becoming ever more effective and efficient. Still, static techniques either have high precision, in which case powerful judgements are hard to achieve automatically, or they use abstractions supporting increased automation, but possibly losing important aspects of the concrete system in the process. Runtime verification has complementary strengths and weaknesses. It combines full precision of the model (including the real deployment environment) with full automation, but cannot judge future and alternative runs. Another drawback of runtime verification can be the computational overhead of monitoring the running system which, although typically not very high, can still be prohibitive in certain settings. In this paper, we propose a framework to combine static analysis techniques and runtime verification with the aim of getting the best of both techniques. In particular, we discuss an instantiation of our framework for the deductive theorem prover KeY, and the runtime verification tool LARVA. Apart from combining static and dynamic verification, this approach also combines the data centric analysis of KeY with the control centric analysis of LARVA. An advantage of the approach is that, through the use of a single specification which can be used by both analysis techniques, expensive parts of the analysis could be moved to the static phase, allowing the runtime monitor to make significant assumptions, dropping parts of expensive checks at runtime. We also discuss specific applications of our approach

Combining traffic sign detection with 3D tracking towards better driver assistance

We briefly review the advances in driver assistance systems and present a real-time version that integrates single view detection with region-based 3D tracking of traffic signs. The system has a typical pipeline: detection and recognition of traffic signs in independent frames, followed by tracking for temporal integration. The detection process finds an optimal set of candidates and is accelerated using AdaBoost cascades. A hierarchy of SVMs handles the recognition of traffic sign types. The 2D detections are then employed in simultaneous 2D segmentation and 3D pose tracking, using the known 3D model of the recognized traffic sign. Thus, we achieve not only 2D tracking of the recognized traffic signs, but we also obtain 3D pose information, which we use to establish the relevance of the traffic sign to the driver. The performance of the system is demonstrated by tracking multiple road signs in real-world scenarios.Radu Timofte, Victor Adrian Prisacariu, Luc Van Gool, and Ian Rei

Integrating object detection with 3d tracking towards a better driver assistance system

Driver assistance helps save lives. Accurate 3D pose is required to establish if a traffic sign is relevant to the driver. We propose a real-time system that integrates single view detection with region-based 3D tracking of road signs. The optimal set of candidate detections is found, followed by AdaBoost cascades and SVMs. The 2D detections are then employed in simultaneous 2D segmentation and 3D pose tracking, using the known 3D model of the recognised traffic sign. We demonstrate the abilities of our system by tracking multiple road signs in real world scenarios.Victor Adrian Prisacariu, Radu Timofte, Karel Zimmermann, Ian Reid, Luc Van Goo

Compositional Reasoning for Multi-Modal Logics

We provide decomposition and quotienting results for multi-modal logic with respect to a composition operator, traditionally used for epistemic models, due to van Eijck et al. (Journal of Applied Non-Classical Logics 21(3–4):397–425, 2011), that involves sets of atomic propositions and valuation functions from Kripke models. While the composition operator was originally defined only for epistemic S5 n models, our results apply to the composition of any pair of Kripke models. In particular, our quotienting result extends a specific result in the above mentioned paper by van Eijck et al. for the composition of epistemic models with disjoint sets of atomic propositions to compositions of any two Kripke models regardless of their sets of atomic propositions. We also explore the complexity of the formulas we construct in our decomposition result

Aerial Mechatronic Systems for Collection of Atmospheric and Environmental Data

Currently, atmospheric and environmental monitoring also requires approaches based on robotic aerial mechatronic systems that can offer the advantages of onboard intelligent sensors. The accelerated dynamics of climate change generate risks that can be prevented by the acquisition, storage, transmission and processing of data taken under static, quasi-statistical and kinetic conditions at lower costs compared to piloted aircraft. The article presents an approach on atmospheric and environmental monitoring using a robotic aerial mechatronic system based on an airship UAV and a classic airborne UAV, launched using a ground-based launch device

Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade

Camera pose estimation is an important problem in computer vision, with applications as diverse as simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques match the current image against keyframes with known poses coming from a tracker, directly regress the pose, or establish correspondences between keypoints in the current image and points in the scene in order to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time, which made it desirable for systems that require online relocalisation. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of simply accepting the camera pose hypothesis produced by RANSAC without question, we make it possible to score the final few hypotheses it considers using a geometric approach and select the most promising one; (ii) we chain several instantiations of our relocaliser (with different parameter settings) together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade, and the individual relocalisers it contains, to achieve effective overall performance. Taken together, these changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a novel way of visualising the internal behaviour of our forests, and use the insights gleaned from this to show how to entirely circumvent the need to pre-train a forest on a generic scene

On the Realizability of Contracts in Dishonest Systems

We develop a theory of contracting systems, where behavioural contracts may be violated by dishonest participants after they have been agreed upon - unlike in traditional approaches based on behavioural types. We consider the contracts of \cite{CastagnaPadovaniGesbert09toplas}, and we embed them in a calculus that allows distributed participants to advertise contracts, reach agreements, query the fulfilment of contracts, and realise them (or choose not to). Our contract theory makes explicit who is culpable at each step of a computation. A participant is honest in a given context S when she is not culpable in each possible interaction with S. Our main result is a sufficient criterion for classifying a participant as honest in all possible contexts