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Found 23 papers, 8 papers with code
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits.
How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?
The results indicate that the deep ensemble-based method has advantages in improving prediction robustness and estimating epistemic uncertainty.
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation
Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue.
Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity
A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors.
SIND: A Drone Dataset at Signalized Intersection in China
Then, the behaviors of traffic light violations in SIND are recorded.
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures.
STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving
Therefore, this paper proposes a novel Multi-modal Hierarchical Transformer network that fuses the vectorized (agent motion) and visual (scene flow, map, and occupancy) modalities and jointly predicts the flow and occupancy of the scene.
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning
Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL.
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.
Single UHD Image Dehazing via Interpretable Pyramid Network
Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time.
Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention Network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations.
RHONN Modelling-enabled Nonlinear Predictive Control for Lateral Dynamics Stabilization of An In-wheel Motor Driven Vehicle
To further advance the lateral stabilization performance of the IMDV, in this paper a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method.
Driving Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections: A Differential Game Approach
To realize human-like driving and personalized decision-making, driving aggressiveness is first defined for AVs.
Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits
To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users.
Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving
A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm.
Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks
In this manuscript, we propose a novel attack detection scheme that leverage real-time sensor/network data and physics-based mathematical models of vehicles in the platoon.
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving
Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance.
A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks
The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side.
A Marker-free Head Tracker Using Vision-based Head Pose Estimation with Adaptive Kalman Filter
To handle the error of the head pose estimation model, this paper proposes an adaptive Kalman Filter.
Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours
To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours.
Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation
This paper proposes a novel Bernoulli heatmap for head pose estimation from a single RGB image.
