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Handling Pedestrian Uncertainty in Coordinating Autonomous Vehicles at Signal-Free Intersections

Filippos N. Tzortzoglou, Andreas A. Malikopoulos

TL;DR

This work tackles the problem of coordinating connected and automated vehicles (CAVs) at signal-free intersections under unexpected pedestrian crossings. It first derives unconstrained trajectories via a two-stage optimization (time-optimal upper level and energy-optimal lower level) and then enforces safety with barrier certificates (CBFs) when constraints are violated. For pedestrian encounters, it introduces a bicycle-model emergency mode with a rotating elliptical unsafe set around the pedestrian, supported by high-order CBFs, steering and jerk constraints, and soft constraints to guide recovery; a real-time resequencing-replanning mechanism updates all CAV trajectories. The framework is validated through MATLAB and RoadRunner simulations, showing safe pedestrian avoidance, coordinated re-planning, and reduced risk without requiring zero standstill distance, thereby enhancing robustness in urban CAV deployments with pedestrian uncertainty.

Abstract

In this paper, we provide a theoretical framework for the coordination of connected and automated vehicles (CAVs) at signal-free intersections, accounting for the unexpected presence of pedestrians. First, we introduce a general vehicle-to-infrastructure communication protocol and a low-level controller that determines the optimal unconstrained trajectories for CAVs, in terms of fuel efficiency and travel time, to cross the intersection without considering pedestrians. If such an unconstrained trajectory is unattainable, we introduce sufficient certificates for each CAV to cross the intersection while respecting the associated constraints. Next, we consider the case where an unexpected pedestrian enters the road. When the CAV's sensors detect a pedestrian, an emergency mode is activated, which imposes certificates related to an unsafe set in the pedestrian's proximity area. Simultaneously, a re-planning mechanism is implemented for all CAVs to accommodate the trajectories of vehicles operating in emergency mode. Finally, we validate the efficacy of our approach through simulations conducted in MATLAB and RoadRunner softwares, which facilitate the integration of sensor tools and the realization of real-time implementation.

Handling Pedestrian Uncertainty in Coordinating Autonomous Vehicles at Signal-Free Intersections

TL;DR

This work tackles the problem of coordinating connected and automated vehicles (CAVs) at signal-free intersections under unexpected pedestrian crossings. It first derives unconstrained trajectories via a two-stage optimization (time-optimal upper level and energy-optimal lower level) and then enforces safety with barrier certificates (CBFs) when constraints are violated. For pedestrian encounters, it introduces a bicycle-model emergency mode with a rotating elliptical unsafe set around the pedestrian, supported by high-order CBFs, steering and jerk constraints, and soft constraints to guide recovery; a real-time resequencing-replanning mechanism updates all CAV trajectories. The framework is validated through MATLAB and RoadRunner simulations, showing safe pedestrian avoidance, coordinated re-planning, and reduced risk without requiring zero standstill distance, thereby enhancing robustness in urban CAV deployments with pedestrian uncertainty.

Abstract

In this paper, we provide a theoretical framework for the coordination of connected and automated vehicles (CAVs) at signal-free intersections, accounting for the unexpected presence of pedestrians. First, we introduce a general vehicle-to-infrastructure communication protocol and a low-level controller that determines the optimal unconstrained trajectories for CAVs, in terms of fuel efficiency and travel time, to cross the intersection without considering pedestrians. If such an unconstrained trajectory is unattainable, we introduce sufficient certificates for each CAV to cross the intersection while respecting the associated constraints. Next, we consider the case where an unexpected pedestrian enters the road. When the CAV's sensors detect a pedestrian, an emergency mode is activated, which imposes certificates related to an unsafe set in the pedestrian's proximity area. Simultaneously, a re-planning mechanism is implemented for all CAVs to accommodate the trajectories of vehicles operating in emergency mode. Finally, we validate the efficacy of our approach through simulations conducted in MATLAB and RoadRunner softwares, which facilitate the integration of sensor tools and the realization of real-time implementation.
Paper Structure (21 sections, 5 theorems, 44 equations, 11 figures)

This paper contains 21 sections, 5 theorems, 44 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Signal-free intersections and CAVs
  3. Pedestrian avoidance
  4. Research gap and contributions of this paper
  5. Organization of this paper
  6. Modeling framework
  7. Vehicle Dynamics
  8. Safety constraints
  9. Communication protocol within the control zone
  10. Low-level controller design for CAVs
  11. Definition of low-level controller
  12. Certificates for constraint satisfaction.
  13. Case of unexpected event
  14. Bicycle kinematic model
  15. Unsafe set associated with the pedestrian
  16. ...and 6 more sections

Key Result

Lemma 1

Consider a CAV $i\in \mathcal{N}$ operating on a path $z\in \mathcal{G}$. Let $\Phi(p_i(t))$ be a continuously differentiable function satisfying the boundary conditions $\Phi(p_i(t_i^0)) = -\frac{\tilde{\gamma}}{v_i^0}$ and $\Phi(p_i(t_i^n)) = \phi$, where $p_i(t_i^0)=0$ is the position of the vehi satisfies the boundary conditions, and the right-hand side of eq:lateral at $t=t_0$ is $\Phi(p_i(t_

Figures (11)

  • Figure 1: Set up of intersection and illustration of rear-end and lateral constraints.
  • Figure 2: Bicycle kinematic model and dynamics for CAV i.
  • Figure 3: Pedestrian surrounded by the ellipse.
  • Figure 4: Flow chart when a CAV enters the emergency mode.
  • Figure 5: Maneuver of CAV for pedestrian avoidance in Scenario 1.
  • ...and 6 more figures

Theorems & Definitions (21)

  • Remark 1
  • Definition 1
  • Definition 2
  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Remark 2
  • Remark 3
  • Remark 4
  • ...and 11 more