Intelligent Intersection Control System (IICS)

• IICS is a multi-agent cyber-physical system that integrates trust modeling, reinforcement learning, and adaptive signaling to optimize intersection safety and efficiency.
• It employs subjective logic to compute per-agent trust scores and dynamically adjusts buffer sizes to reinforce safe behavior and efficient vehicle scheduling.
• Empirical results demonstrate significant collision rate reductions and throughput improvements, validating the system's performance in mixed-trust traffic environments.

An Intelligent Intersection Control System (IICS) is a multi-agent cyber-physical system designed to optimize the safety, efficiency, and adaptability of vehicle and vulnerable road-user movements through road intersections, leveraging advanced sensing, distributed computation, wireless communication, and algorithmic coordination. IICS architectures depart from fixed-schedule signalization by dynamically integrating trust-aware reasoning, reinforcement learning, constraint programming, multi-agent coordination, and hybrid human/machine interaction to achieve performance surpassing that of conventional infrastructure-only schemes. This entry synthesizes the core design paradigms, algorithmic methods, and empirical results drawn primarily from the AIM-Trust trust-aware intersection control framework (Cheng et al., 2021), with cross-reference to key related approaches.

1. System Architecture: Trust Authority and Decision Flow

The AIM-Trust IICS is organized around a cloud- or edge-resident Intersection Manager (IM) paired with a central Trust Authority (TA), supported by Local Trust Authorities (LTAs) at the roadside. The TA maintains a hash table ℋ mapping each agent (vehicle or infrastructure subsystem) identifier to a subjective-logic "opinion" and resulting scalar trust score $p_A$. Each agent queries its current trust $p_A$ from the TA when requesting intersection access or scheduling.

The system workflow is a hierarchical information flow:

• Distributed: LTAs (e.g., RSUs) observe agent behaviors and forward local evidence to the TA.
• Centralized: The TA fuses these opinions using cumulative fusion operators and continually updates per-agent trustworthiness.
• Query/Control Loop: When a vehicle approaches, it communicates with the IM, which accesses ℋ to retrieve $p_A$ and uses this to inform all control decisions (reservation, scheduling, buffer assignment).

This structure provides dynamic, data-driven adaptation to both agent-specific behaviors (e.g., history of compliance) and aggregate intersection state.

2. Subjective Logic and Quantified Trust Modeling

Each agent $A$ is assigned a binomial subjective-logic opinion $\overline{W}_A = \{b_A, d_A, u_A, a_A\}$, calculated from positive evidence $r$ (e.g., repeated compliance, accurate reporting) and negative evidence $s$ (e.g., trajectory violations, rule breach, detected misbehavior): $$b_A = \frac{r}{r+s+\omega}, \quad d_A = \frac{s}{r+s+\omega}, \quad u_A = \frac{\omega}{r+s+\omega}, \quad b_A + d_A + u_A = 1, \quad a_A \text{ is base rate}$$ where $\omega=2$ and $a_A=0.5$ set the prior in absence of evidence.

The scalar trust score is

$p_A^{TA} = b_A^{TA} + u_A^{TA} a_A^{TA} \in [0,1]$

Multiple observed opinions are combined via subjective-logic "cumulative fusion," supporting robust trust assignment under distributed, partially redundant sensing.

3. Reinforcement-Learning-Based Buffer Allocation

AIM-Trust departs from reservation schemes with fixed spatial/temporal buffers per vehicle by letting an RL agent dynamically select buffer sizes $a_t^i$ in a multi-agent RL environment, with the action space defined as $a_t^i\in\{0,1,\dots,a_{\max}\}$ for each approaching vehicle $i$.

• State Representation: For $n$ vehicles, $$s_t = (id_t^1, e_t^1, o_t^1, p_t^1, \dots, id_t^n, e_t^n, o_t^n, p_t^n)$$, where $id$ is identifier; $e, o$ are entry/exit lane indices; $p$ is agent trust.
• Action: Vector of assigned buffers $a_t^i$.
• Reward: For vehicle $i$ at step $t$,

$$r_t^i = \begin{cases} 1 + \lambda (b_{th} - a_t^i), & \text{if no collision} \ -(\tau-1)\left[1 + \lambda (b_{th} - a_t^i)\right], & \text{otherwise} \end{cases}$$

with $b_{th}$ the buffer upper bound, $\lambda>0$ a safety-vs-throughput parameter, and $\tau$ the episode length.

A classical deep Q-learning update is employed: $$Q(s_t,a_t) \leftarrow Q(s_t,a_t) + \alpha[r_{t+1} + \gamma\max_a Q(s_{t+1},a) - Q(s_t,a_t)]$$ Low-trust vehicles receive aggressively larger buffers, pre-empting adversarial or non-compliant behavior.

4. Protocol Extensions and Trust-Driven Reservation

AIM-Trust introduces critical modifications to the classical Autonomous Intersection Management (AIM) protocol:

• Trust-Based Buffer Adjustment: Instead of static buffers ($a=1$), buffer size is computed as $$a^A = \mathrm{buffer\_calculator}(id^A,e^A,o^A,p^A)$$.
• Approve–Observe–Revoke Loop: Post-reservation, the vehicle is surveilled in pre-entry, in-intersection, and post-exit phases. Any detected violation triggers revocation (reservation reset) and negative trust update.
• Decision Policy: Reservations are granted based on simulated trajectories, with buffers and accept/reject logic modulated by $p_A$. Vehicles below a threshold trust are penalized by larger buffers or outright denied entry.

This protocol supports a closed feedback loop for systematic trust updating and adaptive safety enforcement.

5. Empirical Performance and Parameterization

Rigorous simulation-based evaluation covers scenarios with varying proportions of untrusted vehicles. Metrics include collision rate ($c/n\tau$) and throughput ($(n\tau-c)/T$).

Key results:

• Collision rate reductions compared to AIM-1 (fixed buffer): $64\%$ ($20\%$ untrusted), $83\%$ ($40\%$), $79\%$ ($60\%$), $84\%$ ($80\%$), $89\%$ ($100\%$).
• Throughput improvement: On average $15.5\%$ higher than best fixed-buffer competitor across all untrusted ratios.
• In high-trust (predominantly compliant) traffic, performance reduces to that of the efficient unit-buffer AIM scheme.

Parameter selection is explicitly guided:

• $\omega=2$, $a_A=0.5$ yield non-informative priors.
• Large $\lambda$ prioritizes throughput/small buffer, small $\lambda$ is conservative (more safety).
• $b_{th}$ must match worst-case intersection geometry (sight distances, lane count).

6. Safety and Adaptation in Mixed-Trust Environments

The IICS' principal safety mechanism is proactive buffer adaptation conditioned on quantified trustworthiness. Encoding opinions over positive/negative evidence directly into the access and scheduling logic allows the IM to anticipate misbehavior:

• High-trust agents receive throughput-optimal buffers, preserving intersection capacity.
• Low-trust agents are isolated with larger buffers, sacrificing local throughput to saturate safety constraints.

In adversarial or low-trust traffic, the system automatically degrades to a conservative, collision-free mode. Surveillance and enforced revocation with feedback into trust scores maintain resilience against persistent non-cooperation.

7. Interoperability, Generalization, and Implications

The AIM-Trust IICS demonstrates that embedding epistemic trust modeling and reinforcement-based policy synthesis at the intersection infrastructure layer enables adaptivity to variable trust landscapes, scales to mixed-autonomy roadways, and retains backward compatibility with legacy protocols.

This suggests a generalizable methodology for safety–efficiency trade-off optimization in any vehicular multi-agent system where agent trust cannot be statically assumed. The core architecture and algorithmic structure are suitable for extension to corridor- or network-level traffic management via hierarchical trust reasoning and distributed RL controllers.

References: For comprehensive methods, quantitative details, and implementation guidance, see (Cheng et al., 2021).