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Words to Wheels: Vision-Based Autonomous Driving Understanding Human Language Instructions Using Foundation Models

Autor: Ryu, Chanhoe, Seong, Hyunki, Lee, Daegyu, Moon, Seongwoo, Min, Sungjae, Shim, D. Hyunchul

This paper introduces an innovative application of foundation models, enabling Unmanned Ground Vehicles (UGVs) equipped with an RGB-D camera to navigate to designated destinations based on human language instructions. Unlike learning-based methods, t

Externí odkaz: http://arxiv.org/abs/2410.10577

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Enhancing State Estimator for Autonomous Racing : Leveraging Multi-modal System and Managing Computing Resources

Autor: Lee, Daegyu, Nam, Hyunwoo, Ryu, Chanhoe, Nah, Sungwon, Moon, Seongwoo, Shim, D. Hyunchul

Publikováno v: IEEE Transactions on Intelligent Vehicles(2024)

This paper introduces an approach that enhances the state estimator for high-speed autonomous race cars, addressing challenges from unreliable measurements, localization failures, and computing resource management. The proposed robust localization sy

Externí odkaz: http://arxiv.org/abs/2308.07173

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ELiOT : End-to-end Lidar Odometry using Transformer Framework

Autor: Lee, Daegyu, Nam, Hyunwoo, Shim, D. Hyunchul

In recent years, deep-learning-based point cloud registration methods have shown significant promise. Furthermore, learning-based 3D detectors have demonstrated their effectiveness in encoding semantic information from LiDAR data. In this paper, we i

Externí odkaz: http://arxiv.org/abs/2307.11998

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A Versatile Door Opening System with Mobile Manipulator through Adaptive Position-Force Control and Reinforcement Learning

Autor: Kang, Gyuree, Seong, Hyunki, Lee, Daegyu, Shim, D. Hyunchul

The ability of robots to navigate through doors is crucial for their effective operation in indoor environments. Consequently, extensive research has been conducted to develop robots capable of opening specific doors. However, the diverse combination

Externí odkaz: http://arxiv.org/abs/2307.04422

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An Autonomous System for Head-to-Head Race: Design, Implementation and Analysis; Team KAIST at the Indy Autonomous Challenge

Autor: Jung, Chanyoung, Finazzi, Andrea, Seong, Hyunki, Lee, Daegyu, Lee, Seungwook, Kim, Bosung, Gang, Gyuri, Han, Seungil, Shim, David Hyunchul

While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is

Externí odkaz: http://arxiv.org/abs/2303.09463

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Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Autor: Lee, Daegyu, Seong, Hyunki, Han, Seungil, Kang, Gyuree, Shim, D. Hyunchul, Yoon, Yoonjin

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a

Externí odkaz: http://arxiv.org/abs/2210.17302

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A Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

Autor: Lee, Daegyu, Jung, Chanyoung, Finazzi, Andrea, Seong, Hyunki, Shim, D. Hyunchul

This paper describes a resilient navigation and planning system used in the Indy Autonomous Challenge (IAC) competition. The IAC is a competition where full-scale race cars run autonomously on Indianapolis Motor Speedway(IMS) up to 290 km/h (180 mph)

Externí odkaz: http://arxiv.org/abs/2207.12232

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