Decentralized Signal-Free Intersection Management

• Decentralized signal-free intersection management is an approach where connected vehicles locally coordinate their crossing strategies using hard safety constraints for collision avoidance.
• It utilizes a two-tiered optimization framework that integrates decentralized scheduling for merging times with vehicle-level optimal control to minimize travel time and energy consumption.
• The architecture incorporates dynamic resequencing, consensus-based, and auction-inspired protocols to achieve significant improvements in throughput and energy efficiency under urban conditions.

Decentralized Signal-Free Intersection Management is an architectural and algorithmic paradigm for managing vehicle flows at intersections without relying on traditional traffic signals or centralized controllers. In this framework, connected automated vehicles (CAVs) or autonomous vehicles coordinate their crossing strategies locally—with minimal or no explicit intersection-side infrastructure—by adhering to hard safety constraints such as collision avoidance and rear-end gap maintenance. The objective is the real-time, distributed optimization of travel time, energy consumption, and safety under dynamic and possibly congested urban conditions.

1. Formal Problem Definition and Core Principles

At the core of decentralized signal-free intersection management is the two-tiered optimization of CAVs’ behavior when traversing conflict zones. Each vehicle aims to:

• Maximize intersection throughput by minimizing the total span of merging zone occupancy times, subject to first-in-first-out (FIFO) or a dynamically reordered crossing sequence.
• Minimize energy consumption (e.g., fuel or battery use) or a composite objective (e.g., travel time plus energy), by solving an individual optimal control problem consistent with imposed safety constraints.

Let $t_i^m$ (merging zone entry) and $t_i^f$ (exit) denote critical time events for CAV $i$; $p_i(t)$ its position; $u_i(t)$ its acceleration.

Canonical constraints include:

• Rear-end safety: $p_k(t) - p_i(t) \geq \delta$ for all $t$ between the entry and exit of CAV $i$, where $k$ precedes $i$ on the same approach, and $\delta$ is a fixed safety gap.
• Lateral collision avoidance: $[t_i^m, t_i^f] \cap [t_j^m, t_j^f] = \emptyset$ for any $j$ with a conflicting path, ensuring that two potentially colliding CAVs are never present in the merging zone simultaneously.
• Control and state bounds: $u_{\min} \leq u_i(t) \leq u_{\max}$, $v_{\min} \leq v_i(t) \leq v_{\max}$.

This structure renders global signal logic obsolete, replacing it with a distributed protocol where only local schedule and state information is exchanged among vehicles and/or a lightweight coordinator (Malikopoulos et al., 2016).

2. Decentralized Scheduling and Control Architecture

A typical decentralized intersection management architecture employs an upper-level schedule assignment and a lower-level vehicle controller:

• Coordinator role (if present): Assigns each CAV an index and, potentially, crossing priorities or identifies relative positions within lateral and longitudinal conflict sets. The coordinator does not compute controls but provides information such as FIFO order or prior vehicles’ schedules.
• Decentralized scheduling: For each new CAV, feasible merging zone entry times $t_i^m$ are computed recursively based on upstream vehicle timings and conflict resolutions, ensuring safety constraints are always satisfied:

$$t_i^{m,*} = \max\left\{ t_{i-1}^{m,*} + \Delta_{i-1,i},\ t_i^c \right\}$$

with $\Delta_{i-1,i}$ reflecting appropriate rear-end or lateral gaps (Malikopoulos et al., 2016, Zhang et al., 2018).

• Dynamic resequencing: FIFO can be supplanted by local order optimization, where new arrivals dynamically swap places to minimize intersection clearance time, subject to recursive safety and feasibility checks. This step is provably of $O(M)$ per arrival for an $M$-lane intersection (Zhang et al., 2018).
• Vehicle-level optimal control: Once the entry time $t_i^m$ is assigned, each CAV solves:

$$\min_{u_i(\cdot)}\; \int_{t_i^0}^{t_i^m} \frac12 u_i^2(t) dt$$

subject to longitudinal dynamics $\dot{p}_i = v_i$, $\dot{v}_i = u_i$, and all bounds (Malikopoulos et al., 2016, Zhang et al., 2019). The unconstrained optimal solution is cubic in time, patching with saturated (bang–bang) control arcs when constraints activate.

3. Safety Constraints and Feasibility

The safety constraints governing rear-end and lateral separation are “hard” constraints: they are strictly enforced at all times and are central to collision avoidance. Analytical results establish:

• The rear-end constraint admits a feasibility region $\mathcal{F}_i$ in the space of possible entry times and speeds for each vehicle. Analytical verification, including a posteriori checks against the solution trajectory, ensures that the minimum inter-vehicle gap remains above $\delta$ for all $t$ (Malikopoulos et al., 2016).
• When initial states fall outside this region, a feasibility enforcement zone upstream applies a constant deceleration law to correct entry state, guaranteeing that every CAV commences its optimal profile from within the safe region (Zhang et al., 2016).
• For high-traffic regimes, where strictly cubic solutions may become infeasible due to stacked conflict point constraints, higher-order polynomial interpolation of the trajectory can expand the feasible solution set by constructing time-varying profiles that match prescribed crossing slots at multiple points, while minimizing a comfort criterion such as jerk squared (Tzortzoglou et al., 9 Mar 2024).

4. Multi-Objective and Robust Formulations

Beyond strict energy or throughput objectives, the decentralized framework accommodates:

• Weighted multi-objective cost formulations: For example, each CAV can minimize a convex combination of travel time and control energy via

$$\min_{u_i(\cdot)}\, \beta (t_i^m-t_i^0) + (1-\beta) \int_{t_i^0}^{t_i^m} u_i^2(t)\,dt$$

to explore Pareto frontiers between speed and consumption (Zhang et al., 2019, Pan et al., 2022).

• Robust model predictive control (RMPC): To handle model uncertainties and sensor disturbances, tube-based RMPC synthesizes tightened constraint sets, providing provable collision avoidance even in the presence of bounded errors. The overall optimization becomes a sequence of convex second-order cone programs (SOCP), with provable equivalence to the original nonconvex problem (Pan et al., 2022).
• Trade-off analysis: Pareto-efficient solutions show that moderate increases in travel time can substantially reduce energy usage, and that robustness margins (disturbance bounds) can be tuned at the cost of longer travel times (Pan et al., 2022).

5. Algorithmic Variants and Scalability

Several decentralized protocol families address varying deployment and computation requirements:

• Market-inspired auctions: Bidding-based schemes allow vehicles to submit value signals for preferred crossing slots, with intersection-side solvers executing combinatorial winner-determination to allocate non-overlapping reservations (Vasirani et al., 2014, Molinari et al., 2018).
• Consensus-based auctions: Purely decentralized, these V2V algorithms (CBAA-M) run bid/priority max-consensus in small neighboring groups for each conflict point, yielding deadlock-free and collision-free crossing sequences without coordinator infrastructure (Molinari et al., 2018).
• Hybrid and robust extensions: Frameworks accommodate queue-based priority rules, dynamic resequencing for asymmetric loads, and robust optimization under bounded uncertainties (Zhang et al., 2018, Pan et al., 2022).
• Explicit treatment of turns and comfort: Analytical extensions model left/straight/right-turn paths with different geometric and dynamic parameters, and jointly optimize for travel time, energy, and passenger comfort metrics such as jerk (Zhang et al., 2019).
• Platoon-level optimization: Platooning introduces higher-level scheduling, where blocks of CAVs (platoons) coordinate entry as a single entity, further reducing system travel time and fuel by scheduling maximal conflict-free groups and jointly optimizing their movement (Kumaravel et al., 2020).

Scalability is demonstrated by the use of local communications and polynomial-time scheduling/inference protocols, with per-vehicle computational loads independent of global intersection vehicle count (Molinari et al., 2018, Iwase et al., 2022).

6. Performance, Extensions, and Implementation Insights

Extensive simulation studies confirm several system-level benefits:

Metric	Improvement over Signalized Baseline	Remarks
Travel Time	↓30–85% (problem/algorithm dependent)	Results from strict FIFO to optimized platoon schemes
Fuel/Energy Use	↓13–65%	Quadratic cost minimization, momentum conservation
Throughput	↑30–41%	Dynamic resequencing and platoon coordination
Fairness	Adjustable via utility design or auction	Queue/agent grouping influences individual delays

When using purely decentralized operation, system-level control remains robust under mixed fleets (human-driven + CAVs) by designating larger safety margins around non-coordinated vehicles and reverting to basic headway or stop-control as needed (Malikopoulos et al., 2016).

Extensions discussed include support for variable intersection layouts (multi-lane, roundabout, complex urban networks), explicit corridor-level coordination between sequential intersections via distributed constraint optimization (Iwase et al., 2022), and the integration of economic signals or user-differentiation via auction protocols (Vasirani et al., 2014).

7. Open Challenges and Future Directions

Several technical challenges persist:

• Mixed autonomy: Semi-AIMs (autonomous intersection management) require hybrid schemes capable of reverting to traditional signals for human drivers while CAVs maintain decentralized crossing protocols (Zhong et al., 2020).
• Sensor and communication failures: Protocols such as leader-election for virtual traffic lights and blockchain-backed tamper-proof logging are under consideration for failure tolerance and auditability.
• Generalization to high-density traffic: As the domain’s feasibility shrinks at higher densities, algorithmic adaptations such as higher-order trajectory interpolation and adaptive conflict-point scheduling are necessary to preserve throughput and safety (Tzortzoglou et al., 9 Mar 2024).
• Robustness and real-time feasibility: Modern implementations leverage convexified control schemes, tube-based feedback, and small peer-to-peer data exchanges to enable deployment on real CAV fleets within hardware constraints (Pan et al., 2022, Tang et al., 2022).

Research indicates that decentralized, signal-free intersection management paradigms offer demonstrable performance, safety, and scalability advantages, provided the hard collision and gap constraints are systematically enforced and real-time distributed computation is ensured (Malikopoulos et al., 2016, Zhang et al., 2018, Pan et al., 2022).