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Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object detection to avoid collision while its field of view is severely reduced due to the obstruction posed by a large number of road users. Collaborative perception via Vehicle-to-Everything (V2X) communication, which leverages the diverse perspective thanks to the presence at multiple locations of connected agents to form a complete scene representation, is an appealing solution. State-of-the-art V2X methods resolve the performance-bandwidth tradeoff using a mid-collaboration approach where the Bird-Eye View images of point clouds are exchanged so that the bandwidth consumption is lower than communicating point clouds as in early collaboration, and the detection performance is higher than late collaboration, which fuses agents' output, thanks to a deeper interaction among connected agents. While achieving strong performance, the real-world deployment of most mid-collaboration approaches is hindered by their overly complicated architectures, involving learnable collaboration graphs and autoencoder-based compressor/ decompressor, and unrealistic assumptions about inter-agent synchronization. In this work, we devise a simple yet effective collaboration method that achieves a better bandwidth-performance tradeoff than prior state-of-the-art methods while minimizing changes made to the single-vehicle detection models and relaxing unrealistic assumptions on inter-agent synchronization. Experiments on the V2X-Sim dataset show that our collaboration method achieves 98\% of the performance of an early-collaboration method, while only consuming the equivalent bandwidth of a late-collaboration method.

Localization is paramount for autonomous robots. While camera and LiDAR-based approaches have been extensively investigated, they are affected by adverse illumination and weather conditions. Therefore, radar sensors have recently gained attention due to their intrinsic robustness to such conditions. In this paper, we propose RaLF, a novel deep neural network-based approach for localizing radar scans in a LiDAR map of the environment, by jointly learning to address both place recognition and metric localization. RaLF is composed of radar and LiDAR feature encoders, a place recognition head that generates global descriptors, and a metric localization head that predicts the 3-DoF transformation between the radar scan and the map. We tackle the place recognition task by learning a shared embedding space between the two modalities via cross-modal metric learning. Additionally, we perform metric localization by predicting pixel-level flow vectors that align the query radar scan with the LiDAR map. We extensively evaluate our approach on multiple real-world driving datasets and show that RaLF achieves state-of-the-art performance for both place recognition and metric localization. Moreover, we demonstrate that our approach can effectively generalize to different cities and sensor setups than the ones used during training. We make the code and trained models publicly available at //ralf.cs.uni-freiburg.de.

World models, especially in autonomous driving, are trending and drawing extensive attention due to their capacity for comprehending driving environments. The established world model holds immense potential for the generation of high-quality driving videos, and driving policies for safe maneuvering. However, a critical limitation in relevant research lies in its predominant focus on gaming environments or simulated settings, thereby lacking the representation of real-world driving scenarios. Therefore, we introduce DriveDreamer, a pioneering world model entirely derived from real-world driving scenarios. Regarding that modeling the world in intricate driving scenes entails an overwhelming search space, we propose harnessing the powerful diffusion model to construct a comprehensive representation of the complex environment. Furthermore, we introduce a two-stage training pipeline. In the initial phase, DriveDreamer acquires a deep understanding of structured traffic constraints, while the subsequent stage equips it with the ability to anticipate future states. The proposed DriveDreamer is the first world model established from real-world driving scenarios. We instantiate DriveDreamer on the challenging nuScenes benchmark, and extensive experiments verify that DriveDreamer empowers precise, controllable video generation that faithfully captures the structural constraints of real-world traffic scenarios. Additionally, DriveDreamer enables the generation of realistic and reasonable driving policies, opening avenues for interaction and practical applications.

End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations, or technical terms that are not frequently encountered in the training data. This paper presents Contextual Biasing Whisper (CB-Whisper), a novel ASR system based on OpenAI's Whisper model that performs keyword-spotting (KWS) before the decoder. The KWS module leverages text-to-speech (TTS) techniques and a convolutional neural network (CNN) classifier to match the features between the entities and the utterances. Experiments demonstrate that by incorporating predicted entities into a carefully designed spoken form prompt, the mixed-error-rate (MER) and entity recall of the Whisper model is significantly improved on three internal datasets and two open-sourced datasets that cover English-only, Chinese-only, and code-switching scenarios.

Trajectory generation and trajectory prediction are two critical tasks for autonomous vehicles, which generate various trajectories during development and predict the trajectories of surrounding vehicles during operation, respectively. However, despite significant advances in improving their performance, it remains a challenging problem to ensure that the generated/predicted trajectories are realistic, explainable, and physically feasible. Existing model-based methods provide explainable results, but are constrained by predefined model structures, limiting their capabilities to address complex scenarios. Conversely, existing deep learning-based methods have shown great promise in learning various traffic scenarios and improving overall performance, but they often act as opaque black boxes and lack explainability. In this work, we integrate kinematic knowledge with neural stochastic differential equations (SDE) and develop a variational autoencoder based on a novel latent kinematics-aware SDE (LK-SDE) to generate vehicle motions. Our approach combines the advantages of both model-based and deep learning-based techniques. Experimental results demonstrate that our method significantly outperforms baseline approaches in producing realistic, physically-feasible, and precisely-controllable vehicle trajectories, benefiting both generation and prediction tasks.

High assurance of information-flow security (IFS) for concurrent systems is challenging. A promising way for formal verification of concurrent systems is the rely-guarantee method. However, existing compositional reasoning approaches for IFS concentrate on language-based IFS. It is often not applicable for system-level security, such as multicore operating system kernels, in which secrecy of actions should also be considered. On the other hand, existing studies on the rely-guarantee method are basically built on concurrent programming languages, by which semantics of concurrent systems cannot be completely captured in a straightforward way. In order to formally verify state-action based IFS for concurrent systems, we propose a rely-guarantee-based compositional reasoning approach for IFS in this paper. We first design a language by incorporating ``Event'' into concurrent languages and give the IFS semantics of the language. As a primitive element, events offer an extremely neat framework for modeling system and are not necessarily atomic in our language. For compositional reasoning of IFS, we use rely-guarantee specification to define new forms of unwinding conditions (UCs) on events, i.e., event UCs. By a rely-guarantee proof system of the language and the soundness of event UCs, we have that event UCs imply IFS of concurrent systems. In such a way, we relax the atomicity constraint of actions in traditional UCs and provide a compositional reasoning way for IFS in which security proof of systems can be discharged by independent security proof on individual events. Finally, we mechanize the approach in Isabelle/HOL and develop a formal specification and its IFS proof for multicore separation kernels as a study case according to an industrial standard -- ARINC 653.

This paper works on non-autoregressive automatic speech recognition. A unimodal aggregation (UMA) is proposed to segment and integrate the feature frames that belong to the same text token, and thus to learn better feature representations for text tokens. The frame-wise features and weights are both derived from an encoder. Then, the feature frames with unimodal weights are integrated and further processed by a decoder. Connectionist temporal classification (CTC) loss is applied for training. Compared to the regular CTC, the proposed method learns better feature representations and shortens the sequence length, resulting in lower recognition error and computational complexity. Experiments on three Mandarin datasets show that UMA demonstrates superior or comparable performance to other advanced non-autoregressive methods, such as self-conditioned CTC. Moreover, by integrating self-conditioned CTC into the proposed framework, the performance can be further noticeably improved.

Recommender systems are used to provide relevant suggestions on various matters. Although these systems are a classical research topic, knowledge is still limited regarding the public opinion about these systems. Public opinion is also important because the systems are known to cause various problems. To this end, this paper presents a qualitative analysis of the perceptions of ordinary citizens, civil society groups, businesses, and others on recommender systems in Europe. The dataset examined is based on the answers submitted to a consultation about the Digital Services Act (DSA) recently enacted in the European Union (EU). Therefore, not only does the paper contribute to the pressing question about regulating new technologies and online platforms, but it also reveals insights about the policy-making of the DSA. According to the qualitative results, Europeans have generally negative opinions about recommender systems and the quality of their recommendations. The systems are widely seen to violate privacy and other fundamental rights. According to many Europeans, these also cause various societal problems, including even threats to democracy. Furthermore, existing regulations in the EU are commonly seen to have failed due to a lack of proper enforcement. Numerous suggestions were made by the respondents to the consultation for improving the situation, but only a few of these ended up to the DSA.

Hardware-firmware co-verification is critical to design trustworthy systems. While formal methods can provide verification guarantees, due to the complexity of firmware and hardware, it can lead to state space explosion. There are promising avenues to reduce the state space during firmware verification through manual abstraction of hardware or manual generation of hints. Manual development of abstraction or hints requires domain expertise and can be time-consuming and error-prone, leading to incorrect proofs or inaccurate results. In this paper, we effectively combine the scalability of simulation-based validation and the completeness of formal verification. Our proposed approach is applicable to actual firmware and hardware implementations without requiring any manual intervention during formal model generation or hint extraction. To reduce the state space complexity, we utilize both static module-level analysis and dynamic execution of verification scenarios to automatically generate system-level hints. These hints guide the underlying solver to perform scalable equivalence checking using proofs. The extracted hints are validated against the implementation before using them in the proofs. Experimental evaluation on RISC-V based systems demonstrates that our proposed framework is scalable due to scenario-based decomposition and automated hint extraction. Moreover, our fully automated framework can identify complex bugs in actual firmware-hardware implementations.

Defensive deception is a promising approach for cyberdefense. Although defensive deception is increasingly popular in the research community, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.