Vehicle trajectory prediction works, but not everywhere

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Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

Increasing the capability of automated driving vehicles is motivated by environmental, productivity, and traffic safety benefits. But over-reliance on the automation system is known to cause accidents. The role of the driver cannot be underestimated as it ...

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Ever growing pixel acquisition rates in the fields of augmented reality, autonomous driving and robotics have increased interest in solid state beam scanning without moving parts. Modern photonic integrated laser ranging advances towards passive beam steer ...

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

The field of artificial intelligence is set to fuel the future of mobility by driving forward the transition from advanced driver-assist systems to fully autonomous vehicles (AV). Yet the current technology, backed by cutting-edge deep learning techniques, ...

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. ...

Detecting people from 2D images and analyzing their motion in 3D have been long standing computer vision problems central to numerous applications such as autonomous driving and athletic training. Recently, with the availability of large amounts of trainin ...

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. The ...

A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidel ...

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and ve ...

Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots. However, they are usually developed independently and have their respective strengths and limitations. We propose a no ...

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan ...

Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to tackle this problem. However, they do not focus on exploiting different tem ...

While the quality of GAN image synthesis has improved tremendously in recent years, our ability to control and condition the output is still limited. Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edit ...

Vehicle trajectory prediction works, but not everywhere

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Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Haptics based multi-level collaborative steering control for automated driving

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Chaotic microcomb inertia-free parallel ranging

Robust Training and Verification of Deep Neural Networks

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Reshaping Perception for Autonomous Driving with Semantic Keypoints

Pedestrian 3D Bounding Box Prediction

Long Term Motion Prediction Using Keyposes

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

A Shared Representation for Photorealistic Driving Simulators

MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Motion planning for CAVs in mixed traffic, a study on roundabouts

Motion Prediction Using Temporal Inception Module

Editing in Style: Uncovering the Local Semantics of GANs