This page contains links to various code repositories used to perform the experiments presented in my dissertation.

PDF: Enabling Real-Time Certification of Autonomous Driving Applications

The official dissertation document is available through the UNC Library.

Chapter 3: The History vs. Response Time vs. Accuracy Trade-Off

The history-versus-response-time and history-versus-accuracy trade-off experiments can be reproduced using our CodeOcean code capsule.

To extend our experiments or reproduce the entire workflow (e.g., running the scenarios in CARLA, using TensorFlow to detect pedestrians and vehicles, then performing tracking), visit our experiment GitHub repository, which links to other relevant repositories.

Chapter 4: Using NVIDIA GPUs in Real-Time Applications

The GPU scheduling experiments were performed using the CUDA Scheduling Examiner, available from our GitHub repository.

CUPiD^RT is available in a separate GitHub repository.

Chapter 5: Enabling Time Partitioning for Real-Time Multicore+Accelerator Platforms

The code for TimeWall is implemented as a patch against the 5.4.0-rc7 version of LITMUST^RT (commit 55ce628). We also made changes to Liblitmus, available as a patch against the official Liblitmus repository (commit a430c7b).

To run our experiments, we used FeatherTrace; our patch should be applied to the official FeatherTrace repository (commit bea119e).

Our single-node HOG case study was developed using OpenCV. The code is available in a GitHub repository.

Chapter 6: Additional Evaluations

The experiments discussed in this chapter require minor changes to LITMUS^RT and Liblitmus, as well as our Python wrapper for Liblitmus:

The experiments in this chapter use the following applications:

Scripts are available via a zip file.