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Found 16 papers, 2 papers with code
WROOM: An Autonomous Driving Approach for Off-Road Navigation
Off-road navigation is a challenging problem both at the planning level to get a smooth trajectory and at the control level to avoid flipping over, hitting obstacles, or getting stuck at a rough patch.
Synthesis and verification of robust-adaptive safe controllers
Our raCBFs are currently the most effective way to guarantee safety for uncertain systems, achieving 100% safety and up to 55% performance improvement over a robust baseline.
Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning
In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy.
Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation
Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems.
State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections
In this work, we address the problem of traversing unsignalized intersections using a novel curriculum for deep reinforcement learning.
BATS: Best Action Trajectory Stitching
Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ensure the actions of the learned policy are constrained to the logged data.
Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios
We train policies to robustly negotiate with opposing vehicles of an unobservable degree of cooperativeness using multi-agent reinforcement learning (MARL).
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning
The problem of incomplete observations is handled by using a Long-Short-Term-Memory (LSTM) layer in the network.
Safe Trajectory Planning Using Reinforcement Learning for Self Driving
Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and complex behaviors.
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning
In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments.
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera
This late-fusion block uses the dense context features to guide the depth prediction based on demonstrations by sparse depth features.
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
In this work, we propose a hierarchical reinforcement learning (HRL) structure which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Human Driver Behavior Prediction based on UrbanFlow
How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off.
Low-cost LIDAR based Vehicle Pose Estimation and Tracking
Based on our previous optimization/criteria-based L-Shape fitting algorithm, we here propose a data-driven and model-based method for robust vehicle segmentation and tracking.
Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks
In this paper, we leverage auxiliary information aside from raw images and design a novel network structure, called Auxiliary Task Network (ATN), to help boost the driving performance while maintaining the advantage of minimal training data and an End-to-End training method.
Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing
The time complexity of solving MASP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping.
