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

TL;DR

The paper addresses enabling autonomous vehicles to operate in shared urban spaces while complying with traffic laws by modeling a subset of the UK Highway Code for junctions. It proposes a modular, rule-based framework that combines a controlled natural language layer (Logical English), a Prolog rule engine, and a NetLogo multi-agent simulator to reason about junction rules and liability. The approach emphasizes interpretability and parity between human and autonomous drivers, and avoids ML-based perception inputs, instead assuming sensor readings feed a rule base for decision-making. The system supports violation detection, logging, and subsequent legal reasoning to assign penalties, and aims to be extensible across modules and scenarios.

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.

Figures (4)

• Figure 1: Simulated environment
• Figure 2: Action Execution with respect to Legal Rules
• Figure 3: Example of violation detected by the monitor (the arrow)
• Figure 4: Example of an "allowed" violation, i.e., a permission