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Accurate perception is essential for advancing autonomous driving and addressing safety challenges in modern transportation systems. Despite significant advancements in computer vision for object recognition, current perception methods still face difficulties in complex real-world traffic environments. Challenges such as physical occlusion and limited sensor field of view persist for individual vehicle systems. Cooperative Perception (CP) with Vehicle-to-Everything (V2X) technologies has emerged as a solution to overcome these obstacles and enhance driving automation systems. While some research has explored CP's fundamental architecture and critical components, there remains a lack of comprehensive summaries of the latest innovations, particularly in the context of V2X communication technologies. To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies. Additionally, a contemporary generic framework is also proposed to illustrate the V2X-based CP workflow, aiding in the structured understanding of CP system components. Furthermore, this paper categorizes prevailing V2X-based CP methodologies based on the critical issues they address. An extensive literature review is conducted within this taxonomy, evaluating existing datasets and simulators. Finally, open challenges and future directions in CP for autonomous driving are discussed by considering both perception and V2X communication advancements.

Cooperative utilization of Unmanned Aerial Vehicles (UAVs) in public and military surveillance applications has attracted significant attention in recent years. Most UAVs are equipped with sensors that have bounded coverage and wireless communication equipment with limited range. Such limitations pose challenging problems to monitor mobile targets. This paper examines fulfilling surveillance objectives to achieve better coverage while building a resilient network between UAVs with an extended lifetime. The multiple target tracking problem is studied by including a relay UAV within the fleet whose trajectory is autonomously calculated in order to achieve a reliable connected network among all UAVs. Optimization problems are formulated for single-hop and multi-hop communications among UAVs. Three heuristic algorithms are proposed for multi-hop communications and their performances are evaluated. A hybrid algorithm, which dynamically switches between single-hop and multi-hop communications is also proposed. The effect of the time horizon considered in the optimization problem is studied. Performance evaluation results show that the trajectories generated for the relay UAV by the hybrid algorithm can achieve network lifetimes that are within 5% of the maximum possible network lifetime which can be obtained if the entire trajectories of all targets were known a priori.

Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a novel framework called DASC that possesses strong controllability with a weighted decoding paradigm, while improving generation quality with the grounding in an attribute semantics space. Generation with multiple attributes is then intuitively implemented with an interpolation of multiple attribute embeddings, which results in substantial reduction in the model sizes. Experiments show that DASC can achieve high control accuracy in generation task with the simultaneous control of 3 aspects while also producing interesting and reasonably sensible responses, even in an out-of-distribution robustness test.

Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, we propose NashFormer, a framework for trajectory prediction that leverages game-theoretic inverse reinforcement learning to improve coverage of multi-modal predictions. We use a training-time game-theoretic analysis as an auxiliary loss resulting in improved coverage and accuracy without presuming a taxonomy of actions for the agents. We demonstrate our approach on the interactive split of the Waymo Open Motion Dataset, including four subsets involving scenarios with high interaction complexity. Experiment results show that our predictor produces accurate predictions while covering $33\%$ more potential interactions versus a baseline model.

While fine-tuning unleashes the potential of a pre-trained model to a specific task, it trades off the model's generalization capability on out-of-distribution (OOD) datasets. To mitigate this, robust fine-tuning aims to ensure performance on OOD datasets as well as an in-distribution (ID) dataset for which the model is being tuned. However, another criterion for reliable machine learning (ML), confidence calibration, has been overlooked despite its increasing demand for real-world high-stakes ML applications (e.g., autonomous driving and medical diagnosis). For the first time, we raise concerns about the calibration of fine-tuned vision-language models (VLMs) under distribution shift by showing that naive fine-tuning and even state-of-the-art robust fine-tuning methods hurt the calibration of pre-trained VLMs, especially on OOD datasets. To address this, we provide a simple approach, called a calibrated robust fine-tuning (CaRot) that incentivizes the calibration and robustness on both ID and OOD datasets. Empirical results on ImageNet-1K distribution shift evaluation verify the effectiveness of our method.

Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities. However, the uncertain temporal asynchrony and limited communication conditions can lead to fusion misalignment and constrain the exploitation of infrastructure data. To address these issues in vehicle-infrastructure cooperative 3D (VIC3D) object detection, we propose the Feature Flow Net (FFNet), a novel cooperative detection framework. FFNet is a flow-based feature fusion framework that uses a feature flow prediction module to predict future features and compensate for asynchrony. Instead of transmitting feature maps extracted from still-images, FFNet transmits feature flow, leveraging the temporal coherence of sequential infrastructure frames. Furthermore, we introduce a self-supervised training approach that enables FFNet to generate feature flow with feature prediction ability from raw infrastructure sequences. Experimental results demonstrate that our proposed method outperforms existing cooperative detection methods while only requiring about 1/100 of the transmission cost of raw data and covers all latency in one model on the DAIR-V2X dataset. The code is available at \href{//github.com/haibao-yu/FFNet-VIC3D}{//github.com/haibao-yu/FFNet-VIC3D}.

Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-world deployment of autonomous vehicles, it is of critical importance to automatically find simulation scenarios where the driving policies will fail. We propose a method that efficiently generates adversarial simulation scenarios for autonomous driving by solving an optimal control problem that aims to maximally perturb the policy from its nominal trajectory. Given an image-based driving policy, we show that we can inject new objects in a neural rendering representation of the deployment scene, and optimize their texture in order to generate adversarial sensor inputs to the policy. We demonstrate that adversarial scenarios discovered purely in the neural renderer (surrogate scene) can often be successfully transferred to the deployment scene, without further optimization. We demonstrate this transfer occurs both in simulated and real environments, provided the learned surrogate scene is sufficiently close to the deployment scene.

Automotive engine assembly and disassembly are common and crucial programs in the automotive industry. Traditional education trains students to learn automotive engine assembly and disassembly in lecture courses and then to operate with physical engines, which are generally low effectiveness and high cost. In this work, we developed a multi-layer structured Virtual Reality (VR) system to provide students with training in automotive engine (Buick Verano) assembly and disassembly. We designed the VR training system with The VR training system is designed to have several major features, including replaceable engine parts and reusable tools, friendly user interfaces and guidance, and bottom-up designed multi-layer architecture, which can be extended to various engine models. The VR system is evaluated with controlled experiments of two groups of students. The results demonstrate that our VR training system provides remarkable usability in terms of effectiveness and efficiency. Currently, our VR system has been demonstrated and employed in the courses of Chinese colleges to train students in automotive engine assembly and disassembly. A free-to-use executable file (Microsoft Windows) and open-source code are available at //github.com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry. Finally, a video describing the operations in our VR training system is available at //www.youtube.com/watch?v=yZe4YTwwAC4

Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.