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Found 36 papers, 5 papers with code
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.
YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless Automated Diving
This representation can be used to describe lane borders, markings, but also implicit features such as centerlines of lanes.
Self-supervised pseudo-colorizing of masked cells
Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning.
Deep Geometry-Aware Camera Self-Calibration from Video
The self-calibrating bundle adjustment layer optimizes camera intrinsics through classical Gauss-Newton steps and can be adapted to different camera models without re-training.
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e. g., moving from point A to B.
Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings
Localization in aerial imagery-based maps offers many advantages, such as global consistency, geo-referenced maps, and the availability of publicly accessible data.
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Albeit effective, only few works haveimproved the understanding and the performance of weight averaging. Here, we revisit this approach and show that a simple weight fusion (WF)strategy can lead to a significantly improved predictive performance andcalibration.
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources.
Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras.
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations
In particular, we fuse learned features from complementary representations.
Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle.
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function
Since a 3D surface can be usually observed from multiple camera images with different view poses, an optimal image patch selection for the texturing and an optimal semantic class estimation for the semantic mapping are still challenging.
TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis
To calibrate the stereo camera, a photometric error function is builded and the LiDAR depth is involved to transform key points from one camera to another.
Modeling dynamic target deformation in camera calibration
We demonstrate the effectiveness of modeling dynamic deformations using different calibration targets and show its significance in a structure-from-motion application.
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization
We therefore perform a case study of the deployment of two representative object detection networks on an edge AI platform.
Inferring bias and uncertainty in camera calibration
Accurate camera calibration is a precondition for many computer vision applications.
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning
Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging.
Autonomous Vehicles
Distributional Reinforcement Learning
+2
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
At the heart of all automated driving systems is the ability to sense the surroundings, e. g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuScenes-LidarSeg.
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data
The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios.
YOLinO: Generic Single Shot Polyline Detection in Real Time
Reformulating the problem of polyline detection as a bottom-up composition of small line segments allows to detect bounded, dashed and continuous polylines with a single head.
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving.
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information.
RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features
Pedestrian crossing prediction is a crucial task for autonomous driving.
Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data
This representation allows us to use well-studied deep learning architectures from the image domain to predict a dense semantic grid map using only the sparse input data of a single LiDAR scan.
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.
Learned Enrichment of Top-View Grid Maps Improves Object Detection
We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input.
Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups
We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios.
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants.
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.
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences
We apply our approach to optical flow estimation from camera image sequences, validate on odometry estimation and suggest a method to iteratively increase optical flow estimation accuracy using the generated motion masks.
Accurate Global Trajectory Alignment using Poles and Road Markings
To bypass the flaws from direct incorporation of GNSS measurements for geo-referencing, the usage of aerial imagery seems promising.
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.
Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
A detailed environment perception is a crucial component of automated vehicles.
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
Pedestrian Prediction by Planning using Deep Neural Networks
Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles.
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery.
