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Found 14 papers, 1 papers with code
Adversarial Defense Teacher for Cross-Domain Object Detection under Poor Visibility Conditions
Existing object detectors encounter challenges in handling domain shifts between training and real-world data, particularly under poor visibility conditions like fog and night.
PITA: Physics-Informed Trajectory Autoencoder
To resolve this issue, we propose the novel Physics-Informed Trajectory Autoencoder (PITA) architecture, whichincorporates a physical dynamics model into the loss functionof the autoencoder.
LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users.
High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging
Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving.
Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces
In this paper, we utilize variants of MAB heuristics that make Lipschitz continuity assumptions on the outcomes of actions to improve the efficiency of sampling-based planning approaches.
Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning
Reinforcement learning is nowadays a popular framework for solving different decision making problems in automated driving.
Decision-making for automated vehicles using a hierarchical behavior-based arbitration scheme
Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving.
Robotics
Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants
We present our results on an evaluation set of 1000 simulated intersections and achieve 99. 9% accuracy on the topology estimation that takes only 36ms, when utilizing tracked object detections.
Localization in Aerial Imagery with Grid Maps using LocGAN
In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps.
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks.
LIMO: Lidar-Monocular Visual Odometry
Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle.
Robotics Image and Video Processing
An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps
In this paper, we present a new approach to vehicle trajectory prediction based on automatically generated maps containing statistical information about the behavior of traffic participants in a given area.
Momo: Monocular Motion Estimation on Manifolds
In order to apply global localisation methods, a pose prior must be known which can be obtained from visual odometry.
Pedestrian Prediction by Planning using Deep Neural Networks
Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles.
