Connected and Automated Vehicles Symposium
PulseAugur coverage of Connected and Automated Vehicles Symposium — every cluster mentioning Connected and Automated Vehicles Symposium across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
-
New AI system mimics human driving behavior using memory integration
Researchers have developed a novel Neuro-Memory Fuzzy Inference System (NeMeFIS), a machine learning architecture designed to mimic human-like car following behavior. This system integrates five types of human memory to…
-
New model bridges traffic theory and decentralized autonomous vehicle interactions
A new agent-based model has been developed to study mixed-autonomy weaving ramps, offering a microscopic interpretation of the Wardrop model. This framework allows for the analysis of decentralized vehicle interactions …
-
AI framework optimizes urban vehicle communication networks
Researchers have developed an AI-driven framework using Graph Neural Networks (GNNs) to optimize real-time multi-hop relay selection in smart urban NR-V2X networks. This approach models the vehicular network as a graph,…
-
New framework simulates cognitive smart freight corridors using AI
Researchers have developed a new agent-based modeling framework that integrates reinforcement learning and multi-agent reinforcement learning to simulate cognitive smart freight corridors. This framework aims to improve…
-
GNN-based MARL framework reduces traffic shockwaves by 80%
Researchers have developed a new decentralized Multi-Agent Reinforcement Learning (MARL) framework that utilizes a Graph Neural Network (GNN) to manage traffic shockwaves. This approach allows connected and autonomous v…
-
New AI model boosts cooperative platooning for connected vehicles
Researchers have developed a new multi-agent deep reinforcement learning model to improve cooperative platooning for connected and automated vehicles (CAVs) in mixed traffic environments. The model, which integrates QMI…
-
AI-powered GNNs optimize urban V2X relay selection
Researchers have developed a new framework using Graph Neural Networks (GNNs) to improve real-time relay selection for NR-V2X communications in urban environments. This approach models vehicular communication states as …
-
Autonomous vehicle routing strategies analyzed for market efficiency
A new paper explores market efficiency in the context of autonomous vehicle fleets, proposing randomized routing strategies. The research suggests that unpredictable travel times for human-driven vehicles, resulting fro…
-
Diffusion model plans traffic for signal-free intersections
Researchers have developed DSIP, a novel multi-agent motion planning framework that utilizes a generative diffusion process for managing traffic at signal-free intersections. This approach shifts from traditional timed …