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Planning, Perception and Navigation for Intelligent Vehicles </h1>
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<p>IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 25<br/>
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<ahref="#" class="icon solid solo fa-calendar-alt"><spanclass="label">Calender</span></a>19th October 2025<br/>
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<ahref="#" class="icon solid solo fa-globe-europe"><spanclass="label">Location</span></a>Hangzhou, CHINA</p>
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<h1>1st Workshop on <br/>
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From Zero to Vienna in One Week: Generalization in Autonomous Driving</h1>
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<p>2026 IEEE International Conference on Robotics & Automation<br/>
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<ahref="#" class="icon solid solo fa-calendar-alt"><spanclass="label">Calender</span></a>1st June 2026<br/>
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<ahref="#" class="icon solid solo fa-globe-europe"><spanclass="label">Location</span></a>Vienna, Austria</p>
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</p>
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@@ -49,14 +49,15 @@ <h1> 15th Workshop on <br/>
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<h2>About the workshop</h2>
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<p><spanclass="image right"><imgsrc="images/Picture_Car.jpg" alt="" /></span>Recently, intelligent perception and navigation techniques have obtained wide attention areas of autonomous robots and systems.
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Autonomous robots and systems capable of conquering well and partially-structured environments are emerging.
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As we look to the future, robots that are capable of operating in genuinely unstructured and dynamic environments remain a significant challenge.
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In such challenging environments, each robot is expected to learn/adapt their surrounding environment via intelligent sensors (such as optical, vision or acoustic sensors)
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while performing goal-oriented navigation and completing the task with only onboard resources.
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On top of single robot autonomy, diverse capabilities of heterogeneous robots are brought together to achieve enhanced performance in task planning, cooperative teaming, planetary exploration.
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Heterogeneous robots and systems can be evident as physical difference between robots (UAV, UGV, USV, Autonomous Vehicles, etc.)
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or as behavioral difference when robots serve diverse roles in a cooperating team.
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<p><spanclass="image right"><imgsrc="images/Picture_Car.jpg" alt="" /></span> Autonomous driving has made remarkable progress in recent years. Nevertheless, a central question remains: How can autonomous vehicle (AV) systems generalize to new environments or unseen conditions? This workshop aims to critically discuss the relationship between software paradigms and generalization in AVs. Specifically, we will focus on modular architectures with defined perception, prediction, and planning components, as well as end-to-end learning approaches that map sensor data directly to driving actions.
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The goal of this workshop is to provide a scientific deep dive into these software paradigms as well
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as discuss how the different strategies such as open-source development support generalization
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challenges. The workshop will provide the context for evaluating emerging approaches to tackle the
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generalization problem and highlight open challenges. We invite researchers from different domains
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such as autonomous driving, robotics, computer vision as well as practitioners from industry to
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highlight diverse perspectives on theoretical and practical challenges regarding generalization in
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autonomous driving. We will explore questions such as:
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