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Found 70 papers, 24 papers with code
UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework
To verify our algorithm, we conducted experiments on the V2X-Sim and OPV2V datasets.
TMA: Temporal Motion Aggregation for Event-based Optical Flow
Event cameras have the ability to record continuous and detailed trajectories of objects with high temporal resolution, thereby providing intuitive motion cues for optical flow estimation.
Fast and Accurate Object Detection on Asymmetrical Receptive Field
To address the above challenges, based on extensive literature research, this paper analyzes methods for improving and optimizing mainstream object detection algorithms from the perspective of evolution of one-stage and two-stage object detection algorithms.
Sequential Spatial Network for Collision Avoidance in Autonomous Driving
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area.
BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving
In this paper, we propose a CNN-based method that overcomes the limitation by establishing feature correlations between regions in sequential images using variants of attention.
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Safe robot reinforcement learning (SRRL) is a crucial step towards achieving human-robot coexistence.
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive.
Autonomous Driving Simulator based on Neurorobotics Platform
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation
In this paper, we propose LoopDA for domain adaptive nighttime semantic segmentation.
Learning from Symmetry: Meta-Reinforcement Learning with Symmetric Data and Language Instructions
We thus propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetric data and language instructions.
Improving the Safety of 3D Object Detectors in Autonomous Driving using IoGT and Distance Measures
State-of-the-art object detectors are commonly evaluated based on accuracy metrics such as mean Average Precision (mAP).
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e. g., up to (much greater than) 100 FPs generated, or up to approx.
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
We introduce a novel federated learning framework, FedD3, which reduces the overall communication volume and with that opens up the concept of federated learning to more application scenarios in network-constrained environments.
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?
In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy.
Edge-Aided Sensor Data Sharing in Vehicular Communication Networks
Specifically, when the measurement error from the sensors (also referred as measurement noise) is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors.
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
To establish a good foundation for future research in this thread, in this paper, we provide a review for safe RL from the perspectives of methods, theory and applications.
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a supervised 3D detection head.
A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research
As part of the first set of data, which we describe in this paper, we provide camera and LiDAR frames from two overhead gantry bridges on the A9 autobahn with the corresponding objects labeled with 3D bounding boxes.
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i. e., aggregation in layers of roadside units and cloud.
A Survey of Robust 3D Object Detection Methods in Point Clouds
The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges.
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation
This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs.
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control
We derive a graph representation that models the physical structure of the manipulator and combines the robot's internal state with a low-dimensional description of the visual scene generated by an image encoding network.
Time-coded Spiking Fourier Transform in Neuromorphic Hardware
After several decades of continuously optimizing computing systems, the Moore's law is reaching itsend.
Are Attention Networks More Robust? Towards Exact Robustness Verification for Attention Networks
Attention Networks (ATNs) such as Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving.
Evaluating Muscle Synergies with EMG Data and Physics Simulation in the Neurorobotics Platform
We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model.
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation.
Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem
Cutting and Packing problems are occurring in different industries with a direct impact on the revenue of businesses.
Multi-Agent Constrained Policy Optimisation
To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Towards Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network
As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment.
Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation
We evaluate it on our rehab datasets, and observe that the performance degrades significantly from non-rehab to rehab, highlighting the need for these datasets.
Meta-Reinforcement Learning in Broad and Non-Parametric Environments
Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills.
3D Human Pose Regression using Graph Convolutional Network
We propose one such graph convolutional network named PoseGraphNet for 3D human pose regression from 2D poses.
Safety Metrics for Semantic Segmentation in Autonomous Driving
Within the context of autonomous driving, safety-related metrics for deep neural networks have been widely studied for image classification and object detection.
Temp-Frustum Net: 3D Object Detection with Temporal Fusion
Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline.
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions
This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres.
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw
Therefore, the algorithm presented in this paper needs to anticipate and avoid dynamic obstacles, such as pedestrians or bicycles, but also be fast enough in order to work in real-time so that it can adapt to changes in the environment.
Motion Planning
Trajectory Planning
Robotics
FloMo: Tractable Motion Prediction with Normalizing Flows
In our work, we model motion prediction directly as a density estimation problem with a normalizing flow between a noise distribution and the future motion distribution.
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting
Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge.
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
Specifically, we first learn an optimal policy in an uncertain environment with Deep Distributional Reinforcement Learning.
Distributional Reinforcement Learning
Reinforcement Learning (RL)
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving
In this work, we adopt this safety objective for interactive planning.
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning
With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higher-dimensional problems.
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection
Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed.
Ranked #1 on
Robotic Grasping
on Jacquard dataset
A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards
In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model.
PointINet: Point Cloud Frame Interpolation Network
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection
At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points.
Neuron Activation Analysis for Multi-Joint Robot Reinforcement Learning
We analyze the individual neuron activity distribution in the network, introduce a pruning algorithm to reduce network size keeping the performance, and with these dense network representations we spot correlations of neuron activity patterns among networks trained for robot manipulators with different joint number.
Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation
In this paper, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment.
Formalizing Traffic Rules for Machine Interpretability
We contribute a formalized set of traffic rules for single-direction carriageways, such as on highways.
Robotics
AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features
Due to the challenges such as the large number and the tiny size of the pedestrians (e. g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e. g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well.
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments
We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.
Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving
If a policy can handle all counterfactual worlds well, it either has seen similar situations during training or it generalizes well and is deemed to be fit enough to be executed in the actual world.
Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach
This model is the main focus of this work, as its contribution is not limited to engineering but also applicable to neuroscience.
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle
Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications.
Identity Recognition in Intelligent Cars with Behavioral Data and LSTM-ResNet Classifier
Identity recognition in a car cabin is a critical task nowadays and offers a great field of applications ranging from personalizing intelligent cars to suit drivers physical and behavioral needs to increasing safety and security.
Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation
An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i. e. a digital twin.
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
Many optimization methods for generating black-box adversarial examples have been proposed, but the aspect of initializing said optimizers has not been considered in much detail.
Leveraging Semantic Embeddings for Safety-Critical Applications
Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning.
Globally optimal vertical direction estimation in Atlanta World
Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames.
Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems
Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception.
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem.
What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction
Our work shows how neural networks for pedestrian motion prediction can be thoroughly evaluated and our results indicate which research directions for neural motion prediction are promising in future.
Graph Neural Networks for Modelling Traffic Participant Interaction
We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account.
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
We consider adversarial examples for image classification in the black-box decision-based setting.
Towards Robotic Eye Surgery: Marker-free, Online Hand-eye Calibration using Optical Coherence Tomography Images
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation.
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services.
Variational PatchMatch MultiView Reconstruction and Refinement
We formulate the combined multi-view stereo reconstruction and refinement as a variational optimization problem.
