  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 $\mu$Drive, an event-based
Domain-Specific Language (DSL) designed for specifying autonomous vehicle
behaviour. $\mu$Drive 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 $\mu$Drive by integrating it with the Apollo ADS
framework. Our findings show that users can effectively influence Apollo's
planning through $\mu$Drive, assisting ADS in achieving improved compliance
with traffic regulations. The response time for $\mu$Drive commands remains
consistently at the second or millisecond level. This suggests that $\mu$Drive
may help pave the way to more personalizsed and user-centric AV experiences.
