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Found 71 papers, 29 papers with code
The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data
In this article, we present a dataset with detailed manual annotations for different kinds of ghost detections.
Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data
The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles.
Multimodal Object Query Initialization for 3D Object Detection
In experiments, we outperform the state of the art in transformer-based LiDAR object detection on the competitive nuScenes benchmark and showcase the benefits of input-dependent multimodal query initialization, while being more efficient than the available alternatives for LiDAR-camera initialization.
Towards Robust 3D Object Detection In Rainy Conditions
In this way, the detected objects are less affected by the adverse weather in the scene, resulting in a more accurate perception of the environment.
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis
LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications.
Group Regression for Query Based Object Detection and Tracking
We show that the proposed method is applicable to many existing transformer based perception approaches and can bring potential benefits.
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
In particular, the pruned network backbone is trained with synthetically generated images, and our proposed intermediate supervision to mimic the unpruned backbone's output feature map.
Energy-based Detection of Adverse Weather Effects in LiDAR Data
Autonomous vehicles rely on LiDAR sensors to perceive the environment.
RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation
Our resulting RT-K-Net sets a new state-of-the-art performance for real-time panoptic segmentation methods on the Cityscapes dataset and shows promising results on the challenging Mapillary Vistas dataset.
LMR: Lane Distance-Based Metric for Trajectory Prediction
In contrast to the Euclidean Miss Rate, qualitative results show that LMR yields misses for sequences where predictions are located on wrong lanes.
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle.
Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++
Because there is no suitable public data set in which clutter is annotated, we design a method to automatically generate the respective labels.
Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles
We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic.
Exploring Navigation Maps for Learning-Based Motion Prediction
Combined with our method for knowledge distillation, we achieve results that are close to the original HD map-reliant models.
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders.
Ranked #1 on
Node Classification
on BGS
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving.
Transformers for Object Detection in Large Point Clouds
We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture.
Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds
We address this problem by presenting a two-step approach for the detection of condensed vehicle gas exhaust.
MotionMixer: MLP-based 3D Human Body Pose Forecasting
Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained spatial dependencies of the body joints.
Ranked #6 on
Human Pose Forecasting
on Human3.6M
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
We define monocular geometric scene understanding as the combination of two known tasks: Panoptic segmentation and self-supervised monocular depth estimation.
Self-Assessment for Single-Object Tracking in Clutter Using Subjective Logic
Therefore, this work presents a novel method for self-assessment of single-object tracking in clutter based on Kalman filtering and subjective logic.
MEAT: Maneuver Extraction from Agent Trajectories
Additionally, an analysis of the datasets and an evaluation of the prediction models based on the agent dynamics is provided.
Transformers for Multi-Object Tracking on Point Clouds
The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as other data types, such as radar.
Robust 3D Object Detection in Cold Weather Conditions
Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions.
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Gesture recognition is essential for the interaction of autonomous vehicles with humans.
Ranked #1 on
Skeleton Based Action Recognition
on Drive&Act
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention
We therefore propose CRAT-Pred, a multi-modal and non-rasterization-based trajectory prediction model, specifically designed to effectively model social interactions between vehicles, without relying on map information.
Ranked #174 on
Motion Forecasting
on Argoverse CVPR 2020
Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions
In this work, we present an approach for monocular hand-eye calibration from per-sensor ego-motion based on dual quaternions.
Anomaly Detection in Radar Data Using PointNets
On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment.
Fast Rule-Based Clutter Detection in Automotive Radar Data
Each of these effects is described both theoretically and regarding a method for identifying the corresponding clutter detections.
GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies
Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types.
Attention-based Vehicle Self-Localization with HD Feature Maps
We present a vehicle self-localization method using point-based deep neural networks.
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving.
Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions
Further, our online calibration approach is tested on the KITTI odometry dataset, which provides data of a lidar and two stereo camera systems mounted on a vehicle.
Robotics
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
On-Road Motion Planning for Automated Vehicles at Ulm University
It is well known that motion planning is a key technology for autonomous driving.
Autonomous Driving
Motion Planning
Robotics
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets.
Point Transformer
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets.
Ranked #35 on
3D Part Segmentation
on ShapeNet-Part
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving.
DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
This work addresses the problem of point cloud registration using deep neural networks.
Using Machine Learning to Detect Ghost Images in Automotive Radar
We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects.
Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation
Computer vision tasks such as semantic segmentation perform very well in good weather conditions, but if the weather turns bad, they have problems to achieve this performance in these conditions.
Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic
Thus, by embedding classical Kalman filtering into subjective logic, our method additionally features an explicit measure for statistical uncertainty in the self-assessment.
Uncertainty depth estimation with gated images for 3D reconstruction
Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence.
Inferring Spatial Uncertainty in Object Detection
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection.
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production.
Learning Super-resolved Depth from Active Gated Imaging
Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usually restricted to low resolution because of technology or cost limitations.
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving.
Separable Convolutional LSTMs for Faster Video Segmentation
The advantage of video segmentation approaches compared to single image segmentation is that temporal image information is considered, and their performance increases due to this.
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available.
Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
One possibility to still obtain reliable results is to observe the environment with different sensor types, such as camera and lidar, and to fuse the sensor data by means of neural networks, since different sensors behave differently in diverse weather conditions.
Uncertainty Estimation in One-Stage Object Detection
Environment perception is the task for intelligent vehicles on which all subsequent steps rely.
Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution
We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset.
Semantic Segmentation of Video Sequences with Convolutional LSTMs
The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach.
DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks
The map is generated off-line by extracting landmarks from the vehicle's field of view, while the measurements are collected similarly on the fly.
2D Car Detection in Radar Data with PointNets
To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box.
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs.
Ranked #2 on
2D Object Detection
on Clear Weather
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving.
Robotics
Gated2Depth: Real-time Dense Lidar from Gated Images
The proposed replacement for scanning lidar systems is real-time, handles back-scatter and provides dense depth at long ranges.
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Training a deep object detector for autonomous driving requires a huge amount of labeled data.
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
We validate our method on the KITTI object detection benchmark.
Robotics
Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks
We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs).
Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions
In this work, different sensor fusion architectures are compared for good and adverse weather conditions for finding the optimal fusion architecture for diverse weather situations.
Disparity Sliding Window: Object Proposals From Disparity Images
A mathematical derivation clarifies the number of object candidates with respect to parameters such as image and object size.
Detecting Traffic Lights by Single Shot Detection
So far, research in traffic light detection mainly focused on hand-crafted features, such as color, shape or brightness of the traffic light bulb.
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
Experimental results show that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion.
Offline Object Extraction from Dynamic Occupancy Grid Map Sequences
The evaluation of algorithms for object extraction or the training and validation of learning algorithms rely on labeled ground truth data.
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning
Furthermore, the architecture is used to evaluate motion-specific physical models for starting and stopping and video-based pedestrian motion classification.
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
We tackle the problem of object detection and pose estimation in a shared space downtown environment.
Tracking Multiple Vehicles Using a Variational Radar Model
Yet, the increased amount of data raises the demands on tracking modules: measurement models that are able to process multiple measurements for an object are necessary and measurement-to-object associations become more complex.
Signal Processing Robotics Computation
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling
Long-term situation prediction plays a crucial role in the development of intelligent vehicles.
Robotics
