Optimal Driver Warning Generation in Dynamic Driving Environment

TL;DR

This paper introduces an optimal driver warning generation framework that models driver reactions and surrounding vehicle interactions over a long horizon using POMDP, improving warning effectiveness in dynamic environments.

Contribution

It formulates driver warning generation as a POMDP and proposes an optimal solution, addressing limitations of existing rule-based and one-shot warning systems.

Findings

Proposed method outperforms existing warning systems in simulations.

Modeling long-term interactions enhances warning relevance.

Framework adapts to dynamic driving scenarios.

Abstract

The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning technologies, mainly the forward collision warning and unsafe lane change warning, can reduce the risk of collision caused by human errors. However, the current design methods have several major limitations. Firstly, the warnings are mainly generated in a one-shot manner without modeling the ego driver's reactions and surrounding objects, which reduces the flexibility and generality of the system over different scenarios. Additionally, the triggering conditions of warning are mostly rule-based threshold-checking given the current state, which lacks the prediction of the potential risk in a sufficiently long future horizon. In this work, we study the problem of optimally generating driver warnings by considering the interactions among the generated warning, the driver behavior, and the states of ego and surrounding vehicles on a long horizon. The warning generation problem is formulated as a partially observed Markov decision process (POMDP). An optimal warning generation framework is proposed as a solution to the proposed POMDP. The simulation experiments demonstrate the superiority of the proposed solution to the existing warning generation methods.