Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for Autonomous Vehicles

TL;DR

This paper introduces a modular system combining Logical English, Prolog, and multi-agent simulation to model and reason about traffic rules for autonomous vehicles, aiming for a unified approach for human and automated drivers.

Contribution

It presents a novel integrated framework that links natural language, logical reasoning, and simulation to analyze legal traffic rules for autonomous vehicles.

Findings

System enables visualization of traffic rule effects.

Modular design allows component interchangeability.

Supports validation of vehicle compliance and violations.

Abstract

In this paper, we present a modular system for representing and reasoning with legal aspects of traffic rules for autonomous vehicles. We focus on a subset of the United Kingdom's Highway Code (HC) related to junctions. As human drivers and automated vehicles (AVs) will interact on the roads, especially in urban environments, we claim that an accessible, unitary, high-level computational model should exist and be applicable to both users. Autonomous vehicles introduce a shift in liability that should not bring disadvantages or increased burden on human drivers. We develop a system "in silico" of the model. The proposed system is built of three main components: a natural language interface, using Logical English, which encodes the rules; an internal representation of the rules in Prolog; and an multi-agent-based simulation environment, built in NetLogo. The three components interact: Logical English is translated into and out of Prolog (along with some support code); Prolog and NetLogo interface via predicates. Such a modular approach enables the different components to carry different "burdens" in the overall system; it also allows swapping of modules. Given NetLogo, we can visualize the effect of the modeled rules as well as validate the system with a simple dynamic running scenario. Designated agents monitor the behaviour of the vehicles for compliance and record potential violations where they occur. The information on potential violations is then utilized by Validators, to determine whether the violation is punishable, differentiating between exceptions and cases.