Trajectories and Platoon-forming Algorithm for Intersections with Heterogeneous Autonomous Traffic

TL;DR

The paper tackles unsignaled urban intersections with heterogeneous autonomous traffic (cars and trucks) by coupling a platoon-forming scheduler with a speed-profiling controller. It extends polling-model platooning to account for different acceleration capabilities, deriving closed-form, provably safe trajectories that realize platoons near the intersection while maximizing throughput. A capacity-analysis framework links queueing performance (via Loynes stability) to the physical dynamics within a pre-intersection control region, including how control-region length $x_0$ affects feasibility and efficiency. The results demonstrate that a centralized, exhaustively-served platooning approach can substantially improve intersection capacity and safety in mixed-vehicle autonomous fleets, with clear pathways for deployment and future extensions to turns, alternative service disciplines, and networked traffic systems.

Abstract

The anticipated launch of fully autonomous vehicles presents an opportunity to develop and implement novel traffic management systems. Intersections are one of the bottlenecks for urban traffic, and thus offer tremendous potential for performance improvements of traffic flow if managed efficiently. Platoon-forming algorithms, in which vehicles are grouped together with short inter-vehicular distances just before arriving at an intersection at high speed, seem particularly promising in this aspect. In this work, we present an intersection access control system based on platoon-forming for heterogeneous autonomous traffic. The heterogeneity of traffic arises from vehicles with different acceleration capabilities and safety constraints. We focus on obtaining computationally fast and interpretable closed-form expressions for safe and efficient vehicle trajectories that lead to platoon formation, and show that these trajectories are solutions to certain classes of optimisation problems. Additionally, we conduct a numerical study to obtain approximations for intersection capacity as a result of such platoon formation.

Figures (16)

• Figure 1: Capturing safety conditions for a two-lane intersection. Type $0$ vehicles are cars and type $1$ vehicles are trucks.
• Figure 2: Trajectories obtained by solving the joint optimisation problem for a platoon. Blue and red curves represent cars and trucks, respectively.
• Figure 3: Possible trajectories of a car when preceded by a truck.
• Figure 4: Velocity profile of the car in these four cases.
• Figure 5: Case distinction for a car preceded by a truck in its platoon, given that the truck comes to a stop in the control region. The distinction depends on the $\Delta$ quantities for the car and the truck (at $i^{th}$ and $j^{th}$ positions respectively), where $\Delta_i = t_{f,i} - t_{0,i}$.

Theorems & Definitions (12)

• Remark 2.1
• Definition 1: Safety
• Proposition B.1: Safety
• proof
• Proposition B.2: Optimality part 1
• proof
• Corollary B.2.1
• proof
• Corollary B.2.2: Optimality part 2
• proof