V2X-QA provides a view-decoupled benchmark showing infrastructure views aid macroscopic traffic understanding while cooperative reasoning requires explicit cross-view alignment, with V2X-MoE as a routing-based baseline that improves performance.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Ufil introduces a unified open-source framework with standardized interfaces for multi-object tracking that fuses V2X, lidar, and in-road sensor data to achieve lane-level accuracy below 0.3 m lateral RMSE and under 100 ms latency in both simulation and testbed.
RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases where defenses harm performance more than attacks.
A framework trains and compares MLP, transformer, and GAIL-based trajectory models on real driving data, finding that architectural differences cause large variations in robustness to PGD attacks despite similar nominal accuracy.
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RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases where defenses harm performance more than attacks.