Autonomous Driving via Imitation Learning in a Small-Scale Automotive Platform

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dc.contributor.authorWellander, Johan
dc.contributor.authorPetersén, Arvid
dc.contributor.departmentChalmers tekniska högskola / Institutionen för elektrotekniksv
dc.contributor.examinerHammarstrand, Lars
dc.contributor.supervisorEbadi, Hamid
dc.date.accessioned2024-06-25T14:23:40Z
dc.date.available2024-06-25T14:23:40Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractAbstract In recent years, the advancement of autonomous driving (AD) technology has garnered significant interest. Traditionally, AD systems have relied on multiple submodules, each handling specific tasks such as perception, path planning, and vehicle control. However, an emerging alternative is the implementation of end-to-end systems, which directly process sensor input to predict vehicle control. While both reinforcement learning (RL) and imitation learning (IL) are utilized in end-to-end AD systems, RL often finds its strength in simulated environments, where agents learn through exploration and failure. In contrast, IL, learning from an expert model or human, proves more suitable for real-world applications, requiring substantially less data. This thesis presents an implementation of IL for achieving autonomous driving on a go-kart platform. Leveraging both behavioral cloning (BC) and Human Gated Dataset Aggregation (HG-DAgger), we compare the impact of using an interactive IL algorithm HG-DAgger compared to BC. Additionally, our research explores the use of different inputs, including color camera, stereo depth camera, IMU, and the position of ORB features. We also detail the development of a comprehensive software pipeline encompassing data collection, data formatting, model training, and go-kart control. For evaluation, the go-kart was driven around a track for three laps using the trained BC and HG-DAgger models, and assessed based on number of interventions required per lap, distance without accident, lap time, lap time deviation. The results from the evaluation indicate an improvement in performance from using HG-DAgger over BC as well as an improvement from using a stereo depth camera or the position of ORB features as supplementary inputs to the color camera and IMU.
dc.identifier.coursecodeEENX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308038
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectKeywords: Imitation Learning, Behavioral Cloning, Human Gated Dataset Aggregation, Autonomous Driving, ROS2.
dc.titleAutonomous Driving via Imitation Learning in a Small-Scale Automotive Platform
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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