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Found 15 papers, 7 papers with code
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
However, a single parameter set may not deliver the most optimal closed-loop control performance when the context of the MPC operating conditions changes during its operation, urging the need to adapt the cost function weights at runtime.
R$^2$NMPC: A Real-Time Reduced Robustified Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion Control
In this paper, we present a novel Reduced Robustified NMPC (R$^2$NMPC) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified constraints based on the dynamics of ellipsoidal uncertainty sets.
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control
In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints robustification, feasibility, and closed-loop performance.
Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving
We present a novel sensor fusion-based pipeline for offline mapping and online localization based on LiDAR sensors.
A Stochastic Nonlinear Model Predictive Control with an Uncertainty Propagation Horizon for Autonomous Vehicle Motion Control
Our SNMPC approach utilizes Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates nonlinear hard constraints on state expectations and nonlinear probabilistic constraints.
DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles
This research is a work in progress and presents the first concept of establishing a novel perception pipeline.
Game-theoretic Objective Space Planning
Generating competitive strategies and performing continuous motion planning simultaneously in an adversarial setting is a challenging problem.
Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits
It is capable of running simulations of up to eight autonomous vehicles in real time.
Radar Voxel Fusion for 3D Object Detection
This paper develops a low-level sensor fusion network for 3D object detection, which fuses lidar, camera, and radar data.
An Open-Source Scenario Architect for Autonomous Vehicles
The development of software components for autonomous driving functions should always include an extensive and rigorous evaluation.
Robotics Systems and Control Systems and Control
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
Trajectory planning at high velocities and at the handling limits is a challenging task.
Robotics Systems and Control Systems and Control
Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension
One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map.
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
Object detection in camera images, using deep learning has been proven successfully in recent years.
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data
In this paper, we evaluate the performance of a 3D object detection pipeline which is parameterizable with different depth estimation configurations.
Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar
Increasing attention to autonomous passenger vehicles has also attracted interest in an autonomous racing series.
Systems and Control Systems and Control
