13 accepted papers
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X…
To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly detection and segmentation in 2D, research progress in 3D is underexplored. Existing datasets lack high-quality multimodal data that are t…
This paper introduces InverseMatrixVT3D, an efficient method for transforming multi-view image features into 3D feature volumes for 3D semantic occupancy prediction. Existing methods for constructing 3D volumes often rely on depth estimation, device-specific operators, or transformer queries, which…
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are traine…
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brou
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated…
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between m
Semantic segmentation using deep neural networks has been widely explored to generate high-level contextual information for autonomous vehicles. To acquire a complete 180° semantic understanding of the forward surroundings, we propose to stitch semantic images from multiple cameras with varying orie
As autonomous robots increasingly become part of daily life, they will often encounter dynamic environments while only having limited information about their surroundings. Unfortunately, due to the possible presence of malicious dynamic actors, it is infeasible to develop an algorithm that can guara…
Combining multiple sensors for advanced perception is a crucial requirement for autonomous vehicle navigation. Heterogeneous sensors are used to obtain rich information about the surrounding environment. The combination of the camera and lidar sensors enables precise range information that can be pr…
In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural
Vision systems are widely used in autonomous vehicle systems due to the rich information that camera sensors provide of the surrounding environment. This paper presents an automatic algorithm to obtain the drivable path of a vehicle operating in urban roads with or without clear lane markings. The d…
To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high level contextual understanding of the surroundings is necessary to execute accurate driving maneuvers. This paper presents a novel approach to build three d…