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Found 45 papers, 16 papers with code
Object Re-Identification from Point Clouds
To our knowledge, we are the first to study object re-identification from real point cloud observations.
Revealed Multi-Objective Utility Aggregation in Human Driving
A central design problem in game theoretic analysis is the estimation of the players' utilities.
FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios.
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
We evaluate the effectiveness of XC scores via the task of distinguishing true positive (TP) and false positive (FP) detected objects in the KITTI and Waymo datasets.
3D Object Detection
Explainable Artificial Intelligence (XAI)
+1
A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition
In adversarial machine learning, the popular $\ell_\infty$ threat model has been the focus of much previous work.
Interpretable Deep Tracking
By enumerating different tracking decisions and associated reasoning procedures, we can train individual networks to reason about the possible decisions via IIT.
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Detecting OOD inputs is challenging and essential for the safe deployment of models.
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more transferable, generalizable, and robust representation learning.
Ranked #59 on
Motion Forecasting
on Argoverse CVPR 2020
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.
A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles.
A Safety Assurable Human-Inspired Perception Architecture
While research in addressing these limitations is active, in this paper, we argue that a fundamentally different approach is needed to address them.
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components
In this paper, we address the need for defining machine-verifiable reliability requirements for MVCs against transformations that simulate the full range of realistic and safety-critical changes in the environment.
Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning
In this work, after describing and motivating our problem with a simple example, we present a suitable constrained reinforcement learning algorithm that prevents learning instability, using recursive constraints.
A taxonomy of strategic human interactions in traffic conflicts
In order to enable autonomous vehicles (AV) to navigate busy traffic situations, in recent years there has been a focus on game-theoretic models for strategic behavior planning in AVs.
I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames
A particular challenge for both autonomous and human driving is dealing with risk associated with dynamic occlusion, i. e., occlusion caused by other vehicles in traffic.
Generalized dynamic cognitive hierarchy models for strategic driving behavior
While there has been an increasing focus on the use of game theoretic models for autonomous driving, empirical evidence shows that there are still open questions around dealing with the challenges of common knowledge assumptions as well as modeling bounded rationality.
The missing link: Developing a safety case for perception components in automated driving
In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components.
SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection
In this paper, we propose two variants of self-attention for contextual modeling in 3D object detection by augmenting convolutional features with self-attention features.
Ranked #1 on
3D Object Detection
on KITTI Cyclists Hard
Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations
Based on the results, we distill twelve practical design recommendations for AV visual signals, with focus on signal pattern design and placement.
Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving
With autonomous vehicles (AV) set to integrate further into regular human traffic, there is an increasing consensus of treating AV motion planning as a multi-agent problem.
Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector.
Ranked #1 on
3D Object Detection
on KITTI Cyclists Moderate val
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches
In addition to comparing several OODD approaches using our proposed robustness score, we demonstrate that some optimization methods provide better solutions for OODD approaches.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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.
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation
The detection of out of distribution samples for image classification has been widely researched.
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
Out-of-distribution Detection in Classifiers via Generation
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving Confident-Classifiers For Out-of-distribution Detection
In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called “confident-classifier” by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KLdivergence between the predictive distribution of OOD samples in the low-density“boundary” of in-distribution and the uniform distribution (maximizing the entropy of the outputs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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.
MLOD: A multi-view 3D object detection based on robust feature fusion method
The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framework.
Design Space of Behaviour Planning for Autonomous Driving
We explore the complex design space of behaviour planning for autonomous driving.
A Micro-Objective Perspective of Reinforcement Learning
The standard reinforcement learning (RL) formulation considers the expectation of the (discounted) cumulative reward.
FANTrack: 3D Multi-Object Tracking with Feature Association Network
Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN.
Ranked #4 on
3D Multi-Object Tracking
on KITTI
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.
Analysis of Confident-Classifiers for Out-of-distribution Detection
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
Learning a Lattice Planner Control Set for Autonomous Vehicles
This paper introduces a method to compute a sparse lattice planner control set that is suited to a particular task by learning from a representative dataset of vehicle paths.
Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation.
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge.
Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems.
Calibrating Uncertainties in Object Localization Task
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making.
Urban Driving with Multi-Objective Deep Reinforcement Learning
Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should be able to drive to its destination as fast as possible while avoiding collision, obeying traffic rules and ensuring the comfort of passengers.
Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262
In automotive development, safety is a critical objective, and the emergence of standards such as ISO 26262 has helped focus industry practices to address safety in a systematic and consistent way.
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.
An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software
We then provide a set of recommendations on how to adapt the standard to accommodate ML.
Relating Complexity-theoretic Parameters with SAT Solver Performance
Over the years complexity theorists have proposed many structural parameters to explain the surprising efficiency of conflict-driven clause-learning (CDCL) SAT solvers on a wide variety of large industrial Boolean instances.
SAT-based Analysis of Large Real-world Feature Models is Easy
We discovered that a key reason why large real-world FMs are easy-to-analyze is that the vast majority of the variables in these models are unrestricted, i. e., the models are satisfiable for both true and false assignments to such variables under the current partial assignment.
