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Found 40 papers, 15 papers with code
Robust Nonlinear Data-Driven Predictive Control for Mixed Vehicle Platoons via Koopman Operator and Reachability Analysis
Mixed vehicle platoons, comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), hold significant potential for enhancing traffic performance.
Conformal Symplectic Optimization for Stable Reinforcement Learning
Notably, RAD achieves up to a 155. 1% performance improvement over ADAM in Atari games, showcasing its efficacy in stabilizing and accelerating RL training.
Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks
This leads to a robust data-driven predictive control framework, solved in a tube-based control manner.
Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning
End-to-end autonomous driving offers a streamlined alternative to the traditional modular pipeline, integrating perception, prediction, and planning within a single framework.
Unveiling the Black Box: Independent Functional Module Evaluation for Bird's-Eye-View Perception Model
End-to-end models are emerging as the mainstream in autonomous driving perception.
GS-Net: Generalizable Plug-and-Play 3D Gaussian Splatting Module
3D Gaussian Splatting (3DGS) integrates the strengths of primitive-based representations and volumetric rendering techniques, enabling real-time, high-quality rendering.
Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception
Visual bird's eye view (BEV) perception, due to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of urban intelligent driving.
Local Map Construction with SDMap: A Comprehensive Survey
This paper mainly reviews the local map construction methods with SDMap, including definitions, general processing flow, and datasets.
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
Based on prior knowledge about the main composition of the BEV surrounding environment varying with the increase of distance intervals, long-sequence global modeling is utilized to improve the model's understanding and perception of the environment.
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
Perception is essential for autonomous driving system.
Feature Map Convergence Evaluation for Functional Module
Autonomous driving perception models are typically composed of multiple functional modules that interact through complex relationships to accomplish environment understanding.
Neural Radiance Field in Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
Policy Bifurcation in Safe Reinforcement Learning
Safe reinforcement learning (RL) offers advanced solutions to constrained optimal control problems.
Robust Data-EnablEd Predictive Leading Cruise Control via Reachability Analysis
Data-driven predictive control promises model-free wave-dampening strategies for Connected and Autonomous Vehicles (CAVs) in mixed traffic flow.
Reducing Shape-Radiance Ambiguity in Radiance Fields with a Closed-Form Color Estimation Method
The key is a rendering method that is only based on the density field.
CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry
This work presents an accurate and robust method for estimating normals from point clouds.
Training Multi-layer Neural Networks on Ising Machine
This paper proposes an Ising learning algorithm to train quantized neural network (QNN), by incorporating two essential techinques, namely binary representation of topological network and order reduction of loss function.
Distributional Soft Actor-Critic with Three Refinements
To address this issue, we previously proposed the Distributional Soft Actor-Critic (DSAC or DSACv1), an off-policy RL algorithm that enhances value estimation accuracy by learning a continuous Gaussian value distribution.
Learning Optimal Robust Control of Connected Vehicles in Mixed Traffic Flow
Connected and automated vehicles (CAVs) technologies promise to attenuate undesired traffic disturbances.
Cloud Control of Connected Vehicle under Bi-directional Time-varying delay: An Application of Predictor-observer Structured Controller
This article is devoted to addressing the cloud control of connected vehicles, specifically focusing on analyzing the effect of bi-directional communication-induced delays.
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
4D Millimeter-Wave Radar in Autonomous Driving: A Survey
In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving.
Evaluate Geometry of Radiance Fields with Low-frequency Color Prior
From this insight, given a reconstructed density field and observation images, we design a closed-form method to approximate the color field with low-frequency spherical harmonics, and compute the inverse mean residual color.
Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow
This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation.
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification
Self-evolution is indispensable to realize full autonomous driving.
GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation
We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds.
Ranked #4 on
Surface Normals Estimation
on PCPNet
Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic
In this paper, we present the first experimental results of data-driven predictive control for connected and autonomous vehicles (CAVs) in dissipating traffic waves.
DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow
For the control of connected and autonomous vehicles (CAVs), most existing methods focus on model-based strategies.
Experimental Validation of Multi-lane Formation Control for Connected and Automated Vehicles in Multiple Scenarios
Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios.
Multi-vehicle experiment platform: A Digital Twin Realization Method
To confirm effectiveness of this method, a prototype system is developed, which consists of sand table testbed, its twin system and cloud.
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections
This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows.
Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic
In this paper, instead of relying on a parametric car-following model, we introduce a data-driven predictive control strategy to achieve safe and optimal control for CAVs in mixed traffic.
Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency.
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety.
Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability
Most existing strategies for CAVs' longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs' behaviors on upstream traffic flow.
Cooperative Formation of Autonomous Vehicles in Mixed Traffic Flow: Beyond Platooning
In mixed traffic flow consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation.
Leading Cruise Control in Mixed Traffic Flow
Numerical studies confirm the potential of LCC to strengthen the capability of CAVs in suppressing traffic instabilities and smoothing traffic flow.
Systems and Control Systems and Control Optimization and Control
CASNet: Common Attribute Support Network for image instance and panoptic segmentation
Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes.
Distributed Model Predictive Control for Heterogeneous V ehicle Platoons Under Unidirectional Topologies
This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a p r i o r i unknown desired set point.
Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving
Combining deep reinforcement learning and safety based control can get good performance for self-driving and collision avoidance.
Robotics
