26 citations · 32 across the 6 of their papers we have counts for
6 papers
StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
Stepan Konev
Autonomous driving has shifted from modular perception-prediction-planning stacks toward end-to-end (E2E) models that map sensor inputs directly to vehicle control, often regulariz…
From 2D to 3D: AISG-SLA Visual Localization Challenge
Jialin Gao, Bill Ong, Darld Lwi +13
Research in 3D mapping is crucial for smart city applications, yet the cost of acquiring 3D data often hinders progress. Visual localization, particularly monocular camera position…
Kraken: enabling joint trajectory prediction by utilizing Mode Transformer and Greedy Mode Processing
Daniil S. Antonenko, Stepan Konev, Yuriy Biktairov +1
Accurate and reliable motion prediction is essential for safe urban autonomy. The most prominent motion prediction approaches are based on modeling the distribution of possible fut…
Match and Locate: low-frequency monocular odometry based on deep feature matching
Stepan Konev, Yuriy Biktairov
Accurate and robust pose estimation plays a crucial role in many robotic systems. Popular algorithms for pose estimation typically rely on high-fidelity and high-frequency signals…
MPA: MultiPath++ Based Architecture for Motion Prediction
Stepan Konev
Autonomous driving technology is developing rapidly and nowadays first autonomous rides are being provided in city areas. This requires the highest standards for the safety and rel…
MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
Stepan Konev, Kirill Brodt, Artsiom Sanakoyeu
To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task that recent…