ACEC @ WETICE, 27/10/2021

An Adaptive Approach for the Coordination of Autonomous Vehicles at Intersections

Nicholas Glorio, Stefano Mariani, Giacomo Cabri, Franco Zambonelli

Università di Modena e Reggio Emilia

Motivation & Goal

  • current research on self-driving mostly about individual vehicles
  • but cooperative driving is necessary for collective vehicles in many situations
    • e.g. crossing intersections
  • hence we study intersection management
    • compare state of art approaches
    • propose adaptive approach

Intersection crossing: problem

  • decision making process orchestrating vehicles' actions
    • necessary to achieve own goal
    • competitive access to shared resource
  • poses 3 challenges
    • safety: no collisions
    • liveness: right of way for everyone
    • quality: max./min. throughput/delay

Intersection crossing: solutions

  • today: traffic light, precedence rules
    • safe & inefficient
    • efficient & unsafe
  • tomorrow: thanks to V2I / V2V
    • reservation
    • negotiation (e.g. auction)
    • others (e.g. DCOP, game-theoretic, self-org)

Selected approaches

  • precedence-based right of way as baseline
  • centralised reservation-based as "best in class"
    • IM receives space-time reservations for intersection cells from approaching vehicles
    • IM elaborates collision-free trajectories
    • IM communicates back to vehicles their parameters for crossing (e.g. speed profile)
  • decentralised competitive auction as state-of-art
    • vehicles bid for space-time slots within the intersection
    • non-colliding vehicles cross simultaneously
    • a broker collects bids and ranks them to assign the right of way

Performance comparison

  • SUMO simulations, controlled via TraCi API (
  • reservation based on temporal allocations of the intersection area, divided into a grid of occupancy cells [1]
  • English auctions with virtual wallets [2]

[1] Kurt M. Dresner, Peter Stone: A Multiagent Approach to Autonomous Intersection Management. J. Artif. Intell. Res. 31: 591-656 (2008)

[2] Dustin Carlino, Stephen D. Boyles, Peter Stone: Auction-based autonomous intersection management. ITSC 2013: 529-534

Simulation settings

  • single 4-ways junction
  • 3 lanes for each way
  • 1 to 4 vehicles/second (traffic condition)
  • 33/66 % of left turning
  • 30 runs for each params combination


  • no approach works best across traffic conditions
  • no approach works best across performance metrics

Adaptive approach

  • learn which approach works best in which traffic condition
  • apply the best approach known upon changing conditions
  • both with respect to a target performance metric

municipalities already do this!
(e.g. traffic lights turned off at night)

Adaptive Intersection Manager (AIM)

  1. learning phase (naive Q-learning*):
    1. randomly select coordination approach $A$
    2. apply $A$ for a given time $T$
    3. monitor both traffic conditions $C$ and target performance metric $M$ during $T$
  2. adaptation phase:
    1. sample traffic condition $C$
    2. lookup best approach $A$ for $C$ regarding $M$
    3. apply $A$ until $C \rightarrow C'$
    4. loop

* action = application of $a \in A$ during $T$, reward = $M$ measured during $T$


  • the AIM pursues the best $A$ known for every $C$
  • the AIM accounts for the target $M$ to optimise

Conclusion & outlook

  • adaptive approach to intersection crossing presented
  • simulations validate approach empirically
  • promising results despite simplicity
  • extend to more complex measure of $C$
  • extend to more $m \in M$ at once
  • extend to more sophisticated learning


for your attention

Stefano Mariani

Università di Modena e Reggio Emilia