Autonomous Vehicles (AVs) rely on sophisticated Autonomous Driving Systems (ADSs) to provide passengers a satisfying and safe journey. The individual preferences of riders plays a crucial role in shaping the perception of safety and comfort while they are in the car. Existing ADSs, however, lack mechanisms to systematically capture and integrate rider preferences into their planning modules. To bridge this gap, we propose μDrive, an event-based Domain-Specific Language (DSL) designed for specifying autonomous vehicle behaviour. μDrive enables users to express their preferences through rules triggered by contextual events, such as encountering obstacles or navigating complex traffic situations. These rules dynamically adjust the parameter settings of the ADS planning module, facilitating seamless integration of rider preferences into the driving plan. In our evaluation, we demonstrate the feasibility and efficacy of μDrive by integrating it with the Apollo ADS framework. Our findings show that users can effectively influence Apollo's planning through μDrive, assisting ADS in achieving improved compliance with traffic regulations. The response time for μDrive commands remains consistently at the second or millisecond level. This suggests that μDrive may help pave the way to more personalizsed and user-centric AV experiences.
@article{arxiv.2407.13201,
title = {$\mu$Drive: User-Controlled Autonomous Driving},
author = {Kun Wang and Christopher M. Poskitt and Yang Sun and Jun Sun and Jingyi Wang and Peng Cheng and Jiming Chen},
journal= {arXiv preprint arXiv:2407.13201},
year = {2024}
}