Recent studies show that physical adversarial patches (high-contrast stickers or patterns placed on the road or nearby surfaces) can reliably perturb lane segmentation and steering, even when the vehicle is otherwise operating normally. On resource-limited platforms, heavyweight defences are impractical, and naive image filters can degrade nominal performance. At the same time, purely lane-centric pipelines struggle when lanes are partially occluded by vehicles, cones, or pedestrians, lacking object-level context, they may over-trust spurious edges.
This thesis delivers a pragmatic, embedded-friendly robustness strategy with two components. It defines a safe, reproducible physical attack protocol for RaspiCar and benchmarks the existing lane stack under benign and attacked conditions. Furthermore, it implements and tunes low-cost defences, measuring both robustness gains and runtime overhead on-device. To address occlusions and contextual ambiguity, the thesis integrates a TPU-friendly YOLO detector and fuses object cues with lane predictions to down-weight occluded/implausible lane segments and add an “environment confidence” signal for the controller.
The result is the end-to-end characterisation of physical robustness on RaspiCar, paired with deployable defences and scene context that strengthen reliability without violating real-time constraints.