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.
Thesis Type:
- Master Thesis / Master Project
Goal and Tasks:
- Establish a physical patch attack protocol on RaspiCar and deploy lightweight on-device defences that keep robustness high without breaking real-time performance
- Integrate YOLO-based scene context and fuse it with lane perception to reduce interventions during occlusions while preserving nominal driving quality
Demonstrator:
- RaspiCar (Raspberry Pi + Coral/accelerator), camera, existing lane stack, modular ZeroMQ pipeline
- Available attacks/defences baseline
Method:
- Design a safe, reproducible physical-attack evaluation (print files, placements, procedures)
- Implement and tune low-cost defences and measure runtime overhead on-device
- Integrate an edge-friendly YOLO detector; publish detections and fuse with lane outputs to down-weight occluded/implausible segments and emit an environment-confidence signal
- Conduct experiments (baseline vs defence; with/without scene fusion) under benign, occluded, and adversarial conditions; report attack success, lateral error, handovers/km, FPS/latency
Recommended Prior Knowledge:
- Experience with Raspberry Pi or similar embedded platforms
- Programming skills (Python)
- Basic familiarity with computer vision and basic state estimation
- Basic data analysis
Start:
- Flexible – ideally within the next 1–2 months / 6 months
Contact: