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Notes on autonomous vehicles
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NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
Researchers propose NSF-HRPT, a framework that uses a Neural Semantic Field (NSF) to model scene semantics, trajectory predictions, and probabilistic Time-to-Collision (TTC) distributions from simulation data. During…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
A new approach called Master-Agent Proto-plan System (MAPS) is proposed to improve the coordination of autonomous vehicles at unsignalized intersections. MAPS uses a hierarchical deep reinforcement learning…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
The paper introduces a new deep reinforcement learning architecture called Master-Agent Proto-plan System (MAPS) for coordinating autonomous vehicles at unsignalized intersections. The MAPS system uses a centralized…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers present MAPS, a deep reinforcement learning architecture for coordinating autonomous vehicles at unsignalized intersections. The system uses a central Master agent that generates a global coordination…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
TerraZero is a procedural driving simulation developed by researchers to train autonomous driving agents efficiently at scale without the need for real-world data demonstrations. It offers improved speed, realistic…
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
This research focuses on creating a five-stage pipeline to protect pedestrians' privacy in autonomous vehicles (AVs) by using a face-swapping method called Roop, which outperforms Ghost-v2 and strikes a balance between…
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
The paper focuses on creating a five-stage pipeline for protecting pedestrians' privacy in AI models used by autonomous vehicles (AVs) while maintaining the image attributes needed to train the models. The proposed…
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
Researchers propose WCog-VLA, a dual-level model for autonomous driving, which combines semantic world forecasting and generative world evolution to bridge the gap between reactive and proactive driving. The semantic…
Decentralized Autonomous Traffic Management through Corridor Networks
Researchers propose a decentralized approach to managing traffic in Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The method is tested on various multi-corridor networks and…
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