This repository contains the implementation of the algorithm in the paper Where Should We Place LiDARs on the Autonomous Vehicle?-An Optimal Design Approach. We use the perception area and non-detectable subspace to construct the design procedure as solving a min-max optimization problem and propose a bio-inspired measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost function representing the notion of the size of the non-detectable subspaces of a given configuration. We then adopt a cuboid-based approach to show that the proposed VSR-based measure is a well-suited proxy for object detection rate. We use the Artificial Bee Colony (ABC) evolutionary optimization algorithm to yield a tractable cost function computation. The implementation of the ABC algorithm is based on this repo: https://github.com/rwuilbercq/Hive. Our experiments highlight the effectiveness of our proposed VSR measure in terms of cost-effectiveness configuration as well as providing insightful analyses that can improve the design of AV systems.
Run the algorithm with the configuration in the config.yml file.
python main.py
Test the average running time of the solver with the current configuration problem.
python test.py
The Evaluator folder contains the C++ implementation of our solver.
The Evaluator.py is the python interface of the solver.
The Hive folder is the ABC optimization algorithm implemented by https://github.com/rwuilbercq/Hive.
The Result folder saves the running results.