LTAT magistritööd – Master's theses

Selle kollektsiooni püsiv URIhttps://hdl.handle.net/10062/30974

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Edge-Case Handling via Message-Based V2X for Enhanced Vehicle Autonomy
(Tartu Ülikool, 2025) Siniväli, Lisanne; Muhammad, Naveed , juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Autonomous vehicles (AVs) are becoming more common in public traffic, but they still struggle with rare, unpredictable situations (also known as edge cases) that current onboard perception systems often fail to handle. Existing AV systems mainly rely on cameras, LiDAR, and radar to understand their environment, but these sensors are limited by range, field of view, and environmental conditions. Infrastructure-based Vehicle-to-Everything (V2X) communication has been proposed as a solution to address these issues, but many approaches are complex and inefficient. This thesis investigates how AV safety in edge-case scenarios can be improved using a lightweight, event-driven V2X communication layer. The proposed system is based on simplified Decentralized Environmental Notification Messages (DENMs) that are triggered only by critical events. Compared to the usual onboard-only setups, this approach extends the detection range and gives the vehicle more time to react, especially in situations where perception fails or results in a delayed reaction. And since it only sends messages when needed, it avoids network overload while still increasing safety. The results suggest that you do not need a complicated or high-bandwidth system to make AVs safer in tough situations. With the right infrastructure support, even a small addition like this can act as a reliable safety layer and help AVs handle edge cases more confidently.

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