Search Results for author:
Found 14 papers, 6 papers with code
Image-to-Lidar Relational Distillation for Autonomous Driving Data
To bridge this gap, we propose a relational distillation framework enforcing intra-modal and cross-modal constraints, resulting in distilled 3D representations that closely capture the structure of the 2D representation.
JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention
Tracking-by-detection (TBD) methods achieve state-of-the-art performance on 3D tracking benchmarks for autonomous driving.
Class Instance Balanced Learning for Long-Tailed Classification
We propose a novel class instance balanced loss (CIBL), which reweights the relative contributions of a cross-entropy and a contrastive loss as a function of the frequency of class instances in the training batch.
ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation
Unlike existing segmentation methods, ProPanDL is capable of estimating full probability distributions for both the semantic and spatial aspects of panoptic segmentation.
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance.
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks
We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets.
UrbanNet: Leveraging Urban Maps for Long Range 3D Object Detection
Relying on monocular image data for precise 3D object detection remains an open problem, whose solution has broad implications for cost-sensitive applications such as traffic monitoring.
Bayesian Embeddings for Few-Shot Open World Recognition
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information.
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.
Canadian Adverse Driving Conditions Dataset
The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ.
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness.
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception.
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Utilizing open-source tools and resources found in single-player modding communities, we provide a method for persistent, ground truth, asset annotation of a game world.
Joint 3D Proposal Generation and Object Detection from View Aggregation
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
