Personalized Autonomous Driving via Optimal Control with Clearance Constraints from Questionnaires

TL;DR

This paper introduces a personalized autonomous driving planning framework that incorporates user preferences for safe clearance distances through questionnaires, optimizing multiple scenario-specific subproblems in real-time.

Contribution

It presents a novel method to integrate user preferences into autonomous vehicle planning via questionnaires and scenario decomposition for real-time implementation.

Findings

The framework effectively reflects user preferences in simulated driving scenarios.

Scenario decomposition enables real-time personalized planning.

Preference alignment improves compared to baseline planners.

Abstract

Driving without considering the preferred separation distance from surrounding vehicles may cause discomfort for users. To address this limitation, we propose a planning framework that explicitly incorporates user preferences regarding the desired level of safe clearance from surrounding vehicles. We design a questionnaire purposefully tailored to capture user preferences relevant to our framework, while minimizing unnecessary questions. Specifically, the questionnaire considers various interaction-relevant factors, including the surrounding vehicle's size, speed, position, and maneuvers of surrounding vehicles, as well as the maneuvers of the ego vehicle. The response indicates the user-preferred clearance for the scenario defined by the question and is incorporated as constraints in the optimal control problem. However, it is impractical to account for all possible scenarios that may arise in a driving environment within a single optimal control problem, as the resulting computational complexity renders real-time implementation infeasible. To overcome this limitation, we approximate the original problem by decomposing it into multiple subproblems, each dealing with one fixed scenario. We then solve these subproblems in parallel and select one using the cost function from the original problem. To validate our work, we conduct simulations using different user responses to the questionnaire. We assess how effectively our planner reflects user preferences compared to preference-agnostic baseline planners by measuring preference alignment.