Analysis, Modeling, and Simulation (AMS) Case Studies of Connected and Automated Vehicle (CAV) Implementations Specific to the South Central Region

Connected and automated vehicles (CAVs) offer potentially transformative and far-reaching impacts to the transportation system. However, realized benefits will be directly tied to how well agencies prepare for these technologies. This report documents efforts that support CAV preparatory actions in Louisiana and includes: (1) conducting a stakeholder survey to inform engagement activities to develop strategic partnerships in CAV deployment and (2) conducting crash analyses for deployment scenarios of CAV-based queue warning systems (QWSs). An electronic survey was developed and disseminated to 273 Louisiana organizations. The purpose of the survey was to engage these organizations under the context of CAV planning and gauge their awareness, perception, and viewed importance of planning for CAV technologies. Survey results were clustered in three main groups: Group A—those uninformed of CAV technologies and do not believe they will impact their organization, Group B—those more informed but also do not believe their organization will be impacted, and Group C—those aware, positively perceive, and believe it is important to prepare. Results indicate a strong correlation between the level of awareness and perception of CAV technologies. Low awareness and perception by economic development, freight, and transit groups indicate areas of concern. Survey results were further analyzed utilizing a CAV-specific capability maturity framework, and recommendations were developed to engage stakeholders in planning efforts. A crash analysis was conducted at four proposed locations across Louisiana to determine QWS suitability. The analysis utilized five-year historical crash data and focused on crash rate, severity level, manner of collision, and level of service of safety. Due to overrepresented rear-end crashes, QWSs may be suitable at the Jefferson Parish and West Baton Rouge Parish locations. Each effort was prepared to be general and beneficial to transportation agencies involved in similar CAV activities

Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations. We propose to study convergence to approximate local maxima because we show that global convergence is statistically intractable even for one-layer neural net bandit with a deterministic reward. For both nonlinear bandit and RL, the paper presents a model-based algorithm, Virtual Ascent with Online Model Learner (ViOL), which provably converges to a local maximum with sample complexity that only depends on the sequential Rademacher complexity of the model class. Our results imply novel global or local regret bounds on several concrete settings such as linear bandit with finite or sparse model class, and two-layer neural net bandit. A key algorithmic insight is that optimism may lead to over-exploration even for two-layer neural net model class. On the other hand, for convergence to local maxima, it suffices to maximize the virtual return if the model can also reasonably predict the size of the gradient and Hessian of the real return.Comment: Added an instantiation (Example 4.3) of the RL theorem (Theorem 4.4) and more reference

HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer

Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share information to see through occlusions, greatly enhancing perception performance. Nevertheless, existing works all focused on homogeneous traffic where vehicles are equipped with the same type of sensors, which significantly hampers the scale of collaboration and benefit of cross-modality interactions. In this paper, we investigate the multi-agent hetero-modal cooperative perception problem where agents may have distinct sensor modalities. We present HM-ViT, the first unified multi-agent hetero-modal cooperative perception framework that can collaboratively predict 3D objects for highly dynamic vehicle-to-vehicle (V2V) collaborations with varying numbers and types of agents. To effectively fuse features from multi-view images and LiDAR point clouds, we design a novel heterogeneous 3D graph transformer to jointly reason inter-agent and intra-agent interactions. The extensive experiments on the V2V perception dataset OPV2V demonstrate that the HM-ViT outperforms SOTA cooperative perception methods for V2V hetero-modal cooperative perception. We will release codes to facilitate future research

Alphabet of one-loop Feynman integrals

In this paper, we present the universal structure of the alphabet of one-loop Feynman integrals. The letters in the alphabet are calculated using the Baikov representation with cuts. We consider both convergent and divergent cut integrals and observe that letters in the divergent cases can be easily obtained from convergent cases by applying certain limits. The letters are written as simple expressions in terms of various Gram determinants. The knowledge of the alphabet enables us to easily construct the canonical differential equations of the $d\log$ form and aids in bootstrapping the symbols of the solutions.Comment: 13 pages, 2 figures; v3: published version in Chinese physics