REoCAS @ ISOLA, 27/10/2021

Degrees of Autonomy in Coordinating Collectives of Self-Driving Vehicles

Stefano Mariani and 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 collectives of vehicles in many situations
    • e.g. crossing intersections, platoon formation, fleet management, ...
  • cooperative driving demands for coordination
    • we overview key issues in coordinating self-driving vehicles
    • we focus on intersection crossing and categorise existing approaches
    • we argue for an adjustable autonomy approach
    • we overview key challenges for this

Key issues

  • coordinating vehicles $\rightarrow$ decision making process orchestrating vehicles' actions
    • necessary to achieve own and shared ones
    • competitive/collaborative, resource/task -oriented
  • poses 3 challenges
    • safety: no collisions, usually
    • liveness: problem specific*
    • quality: problem specific*

*e.g. intersection crossing $\rightarrow$ no waiting forever, max throughput

Problems overview

resource-oriented task-oriented
competitive intersection crossing, parking (private) ride sharing (private), ramp merging
collaborative parking (fleets), traffic flow optimisation ride sharing (fleets), platooning

Focus on: intersection crossing

  • safety $\rightarrow$ no collisions
  • liveness $\rightarrow$ each vehicle eventually gets right of way
  • quality $\rightarrow$ min. cumulative delay / max. crossing throughput

Autonomy-based categorisation

Approaches categorised based on degree of autonomy left to vehicles during coordination

i.e. to what extent vehicles can decide their own actions

Categories of approaches

  • centralised
    • burden of coordination onto a single entity (Intersection Manager)
    • every vehicles abides to IM decisions by design
  • negotiation-based
    • burden distributed amongst vehicles (and possibly a broker)
    • fixed coordination protocol, fixed goal, fixed strategies
  • agreement-based
    • burden distributed amongst vehicles
    • dynamic coordination protocol, dynamic goal, switching strategies
  • emergent
    • burden distributed amongst vehicles
    • no explicit protocol, no shared goals

Intersection crossing: literature

  • centralised: the IM decides
    • receives movement parameters from approaching vehicles
    • elaborates collision-free trajectories
    • communicates back to vehicles their new parameters for crossing
    • e.g. constrained optimisation, coop. cruise control, reservation-based
  • negotiation: competitive problem $\rightarrow$ auctions
    • 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
    • many variations depending on kind of auction (e.g. English vs. Dutch), bidding strategy, ranking policy, ...
  • agreement: (un)structured interactions
    • e.g. DCOP, argumentation-based coordination, repeated coordination games, ...
  • emergent: no explicit coordination protocol
    • e.g. game-theoretic approaches, self-organisation

Intersection crossing: discussion

  • centralised
    • easiest to rigorously engineer and formally verify
    • guarantees of safety and liveness often available
    • the IM is a bottleneck, and demands for dedicated infrastructure
  • negotiation-based
    • still formally verifiable as protocol is fixed
    • can still guarantee safety if vehicles comply (no attackers)
    • liveness can be an issue especially for auctions
    • neutrality issues may arise (the rich always win)
  • agreement-based
    • not much literature in this category
    • usually difficult to implement, analyse, and control
    • but may provide several benefits, e.g. flexibility and explainability
  • emergent
    • difficult to guarantee any property
    • potentially relevant in mixed scenarios (i.e. with human drivers too)

Why adjustable autonomy

A single approach can hardly fit all possible run-time situations

  • low traffic $\rightarrow$ negotiation/agreement based
    • more autonomy to vehicles
    • still manageable in number of messages
  • high traffic $\rightarrow$ centralised approaches
    • less autonomy to vehicles
    • strict control to avoid collisions and delays

Thus: switch 'em!

How adjustable autonomy

  • Open issue in cooperative driving, proposals in robotics and MAS
  • Several challenges (akin to MAPE-K loop)
    • learn best approach for each problem and specific situation
    • have functional and non-functional performance metrics available to decide
    • decide when to switch, possibly before degradation of efficacy
    • decide how to switch (can't "pause" vehicles)

Further challenges

Besides adjustable autonomy, cooperative coordinated driving still in its infancy

  • system of systems coordination (e.g. "butterfly effects" in intersection networks)
  • dynamic pricing mechanisms may arise, threatening fairness and road neutrality
  • dealing with mixed scenarios, as level 5 self-driving will not replace legacy vehicles overnight

Conclusion & outlook

  • framed traffic-related problems into coordination concepts
  • proposed autonomy-based categorisation of solution approaches
  • argued for adjustable autonomy in coordinated cooperative driving
  • propose practical approach to adjustable autonomy
  • further analyse mixed scenarios


for your attention

Stefano Mariani

Università di Modena e Reggio Emilia