To our knowledge, we are the first to study object re-identification from real point cloud observations.
In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios.
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.
In adversarial machine learning, the popular $\ell_\infty$ threat model has been the focus of much previous work.
By enumerating different tracking decisions and associated reasoning procedures, we can train individual networks to reason about the possible decisions via IIT.
Detecting OOD inputs is challenging and essential for the safe deployment of models.
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
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.
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles.
While research in addressing these limitations is active, in this paper, we argue that a fundamentally different approach is needed to address them.
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.
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.
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.
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.
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.
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.
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
Based on the results, we distill twelve practical design recommendations for AV visual signals, with focus on signal pattern design and placement.
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.
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
In addition to comparing several OODD approaches using our proposed robustness score, we demonstrate that some optimization methods provide better solutions for OODD approaches.
The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ.
The detection of out of distribution samples for image classification has been widely researched.
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
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).
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness.
The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framework.
We explore the complex design space of behaviour planning for autonomous driving.
The standard reinforcement learning (RL) formulation considers the expectation of the (discounted) cumulative reward.
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
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception.
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
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.
Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation.
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge.
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems.
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.
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.
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.
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.
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.
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.