Safe planning using mixed-integer programming for autonomous vehicles coordination

Coordination between intelligent vehicles is essential for the advancement of fully autonomous driving. Ensuring safety is the primary focus of this challenge. This paper proposes a safe model predictive planner (MPP) for vehicle coordination, which is robust against uncertainty and noise. The plann...

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Vydané v:Robotics and autonomous systems Ročník 193; s. 105078
Hlavní autori: Samada, Sergio E., Puig, Vicenç, Nejjari, Fatiha, Sarrate, Ramon
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.11.2025
Predmet:
Coordination
Linear parameter-varying models
Mixed-integer programming
Model predictive planner
Obstacle avoidance
Model predictive planner
Linear parameter-varying models
Coordination
Obstacle avoidance
Mixed-integer programming
ISSN:0921-8890
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Shrnutí:Coordination between intelligent vehicles is essential for the advancement of fully autonomous driving. Ensuring safety is the primary focus of this challenge. This paper proposes a safe model predictive planner (MPP) for vehicle coordination, which is robust against uncertainty and noise. The planner provides a feasible route, taking into account the tube of possible trajectories of neighboring vehicles. To achieve this objective, a linear parameter-varying (LPV) prediction model of the vehicle is used. For obstacle avoidance and overtaking maneuvers, mixed-integer linear inequalities as constraints in the MPP formulation are added. Regarding uncertainty and noise, both are assumed to be unknown but bounded and zonotopes are used to enclose and propagate them. Similarly, a zonotopic optimal filter compensates for the measurement noises and estimates the lateral velocity not provided by the vehicle’s instrumentation. The proposed coordination approach is evaluated in a simulation environment, specifically in an aggressive regime with maximum velocity, using 1/10 scale electric cars. •A safe model predictive planning (MPP) approach is proposed for autonomous vehicles coordination.•The prediction stage uses the LPV representation of the vehicle model which includes dynamic and kinematic relations.•The MPP is robust against modeling errors and noises, to this end, zonotopic sets are used.•Mixed-integer linear inequalities are incorporated to the planner design to address obstacle avoidance.•The methodology is assessed in aggressive regimes of autonomous driving using 1/10 scale simulated cars.
ISSN:0921-8890
DOI:10.1016/j.robot.2025.105078