Structural attention-based recurrent variational autoencoder for highway vehicle anomaly detection
In autonomous driving, detection of abnormal driving behaviors is essential to ensure the
safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that …
safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that …
An expert ensemble for detecting anomalous scenes, interactions, and behaviors in autonomous driving
As automated vehicles enter public roads, safety in a near-infinite number of driving
scenarios becomes one of the major concerns for the widespread adoption of fully …
scenarios becomes one of the major concerns for the widespread adoption of fully …
PeRP: Personalized residual policies for congestion mitigation through co-operative advisory systems
Intelligent driving systems can be used to mitigate congestion through simple actions, thus
improving many socioeconomic factors such as commute time and gas costs. However …
improving many socioeconomic factors such as commute time and gas costs. However …
Cooperative Advisory Residual Policies for Congestion Mitigation
Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus
improving many socioeconomic factors such as commute times and gas costs. However …
improving many socioeconomic factors such as commute times and gas costs. However …
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Deep reinforcement learning (DRL) provides a promising way for intelligent agents (eg,
autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural …
autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural …
Robust driving policy learning with guided meta reinforcement learning
Although deep reinforcement learning (DRL) has shown promising results for autonomous
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …
Task-Driven Autonomous Driving: Balanced Strategies Integrating Curriculum Reinforcement Learning and Residual Policy
Achieving fully autonomous driving in urban traffic scenarios is a significant challenge that
necessitates balancing safety, efficiency, and compliance with traffic regulations. In this …
necessitates balancing safety, efficiency, and compliance with traffic regulations. In this …
Probabilistic Game Theory and Stochastic Model Predictive Control-Based Decision Making and Motion Planning in Uncontrolled Intersections for Autonomous …
Y Jeong - IEEE Transactions on Vehicular Technology, 2023 - ieeexplore.ieee.org
This paper presents a motion planner of autonomous vehicles for uncontrolled intersection
driving. Uncertainty about the surrounding target vehicles is an important consideration for …
driving. Uncertainty about the surrounding target vehicles is an important consideration for …
An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning
Y Liu, S Diao - PLoS one, 2024 - journals.plos.org
As autonomous driving technology continues to advance and gradually become a reality,
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …
HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments
We study the problem of robot navigation in dense and interactive crowds with
environmental constraints such as corridors and furniture. Previous methods fail to consider …
environmental constraints such as corridors and furniture. Previous methods fail to consider …