Hyper-V2X uses a Bayesian hypernetwork with partial weight generation and V2X context embedding to produce calibrated epistemic and aleatoric uncertainty estimates for multi-agent BEV segmentation on the OPV2V benchmark.
A Study of Reinforcement Learning Techniques for Path Tracking in Autonomous Vehicles
8 Pith papers cite this work. Polarity classification is still indexing.
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UniTrans pretrains a bank of translator experts and learns combination coefficients from modality mappings in a scene-invariant latent space to enable zero-shot any-to-any feature translation for heterogeneous collaborative perception.
sumo3Dviz is an open-source lightweight 3D visualization pipeline for SUMO traffic microsimulations that supports batch video generation and first-person perspectives.
A behavior-constrained RL framework with receding-horizon credit assignment learns high-performance control policies that stay aligned with expert behavior in race car simulation.
WLDS applies large models with factual and logical calibration to produce diverse text-and-image deductions of emergency scenarios beyond what traditional fixed simulations can generate.
CCNETS is a new modular causal framework using three cooperative modules and a Zoint mechanism to align synthetic data generation with classifier needs on imbalanced pattern recognition tasks.
LLM-assisted pipeline jointly generates logical formulas and executable predicates for rule-based verification of HD map transformations in CommonRoad, evaluated on synthetic bridge and slope scenarios.
A literature review and industry survey on SDV security and privacy produces a framework for addressing mixed-criticality systems, layered defenses, privacy techniques, and harmonized vehicle-cloud protections.
citing papers explorer
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Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation
Hyper-V2X uses a Bayesian hypernetwork with partial weight generation and V2X context embedding to produce calibrated epistemic and aleatoric uncertainty estimates for multi-agent BEV segmentation on the OPV2V benchmark.
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One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception
UniTrans pretrains a bank of translator experts and learns combination coefficients from modality mappings in a scene-invariant latent space to enable zero-shot any-to-any feature translation for heterogeneous collaborative perception.
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sumo3Dviz: A three dimensional traffic visualisation
sumo3Dviz is an open-source lightweight 3D visualization pipeline for SUMO traffic microsimulations that supports batch video generation and first-person perspectives.
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Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
A behavior-constrained RL framework with receding-horizon credit assignment learns high-performance control policies that stay aligned with expert behavior in race car simulation.
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What Will Happen Next: Large Models-Driven Deduction for Emergency Instances
WLDS applies large models with factual and logical calibration to produce diverse text-and-image deductions of emergency scenarios beyond what traditional fixed simulations can generate.
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CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets
CCNETS is a new modular causal framework using three cooperative modules and a Zoint mechanism to align synthetic data generation with classifier needs on imbalanced pattern recognition tasks.
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LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations
LLM-assisted pipeline jointly generates logical formulas and executable predicates for rule-based verification of HD map transformations in CommonRoad, evaluated on synthetic bridge and slope scenarios.
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Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives
A literature review and industry survey on SDV security and privacy produces a framework for addressing mixed-criticality systems, layered defenses, privacy techniques, and harmonized vehicle-cloud protections.