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Found 11 papers, 0 papers with code
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
Advancements in autonomous driving have increasingly focused on end-to-end (E2E) systems that manage the full spectrum of driving tasks, from environmental perception to vehicle navigation and control.
Physically Analyzable AI-Based Nonlinear Platoon Dynamics Modeling During Traffic Oscillation: A Koopman Approach
Based on that, the routine of linear dynamical system analysis can be conducted on the learned traffic linear dynamics in the embedding space.
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency.
The expressway network design problem for multiple urban subregions based on the macroscopic fundamental diagram
To address the challenges, this paper proposes an expressway network design method for multiple urban subregions based on the macroscopic fundamental diagram (MFD).
Cooperative Route Guidance and Flow Control for Mixed Road Networks Comprising Expressway and Arterial Network
Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short.
Beacon-enabled TDMA Ultraviolet Communication Network System Design and Realization
Nonline of sight (NLOS) ultraviolet (UV) scattering communication can serve as a good candidate for outdoor optical wireless communication (OWC) in the cases of non-perfect transmitter-receiver alignment and radio silence.
Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms
This study presents a bus travel feature extraction method rooted in Point of Interest (POI) data, employing enhanced P-KMENAS and P-LDA algorithms to overcome these limitations.
A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction
PERL contains a physics model and a residual learning model.
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks
Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix.
A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning
An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers.
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs) longitudinal control for partially connected and automated traffic environment based on deep reinforcement learning (DRL) algorithm, which enhances the string stability of mixed traffic, car following efficiency, and energy efficiency.
