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Found 12 papers, 10 papers with code
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored.
Ranked #1 on
Trajectory Prediction
on nuScenes
(using extra training data)
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs.
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
In fact, conventional forecasting methods are usually not trained nor tested in real-world pipelines (e. g., with upstream detection, tracking, and mapping modules).
Unsupervised Object Localization: Observing the Background to Discover Objects
This way, the salient objects emerge as a by-product without any strong assumption on what an object should be.
OCTET: Object-aware Counterfactual Explanations
We conduct a set of experiments on counterfactual explanation benchmarks for driving scenes, and we show that our method can be adapted beyond classification, e. g., to explain semantic segmentation models.
LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation
Recent works in autonomous driving have widely adopted the bird's-eye-view (BEV) semantic map as an intermediate representation of the world.
Ranked #6 on
Bird's-Eye View Semantic Segmentation
on nuScenes
STEEX: Steering Counterfactual Explanations with Semantics
In this work, we address the problem of producing counterfactual explanations for high-quality images and complex scenes.
Raising context awareness in motion forecasting
Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history.
LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR
In such a monocular setup, dense depth is obtained with either additional input from one or several expensive LiDARs, e. g., with 64 beams, or camera-only methods, which suffer from scale-ambiguity and infinite-depth problems.
Explainability of deep vision-based autonomous driving systems: Review and challenges
The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application.
Driving Behavior Explanation with Multi-level Fusion
In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions.
Learning Multi-Modal Word Representation Grounded in Visual Context
Representing the semantics of words is a long-standing problem for the natural language processing community.
