cs.MAMar 20, 2026

Planning Autonomous Vehicle Maneuvering in Work Zones Through Game-Theoretic Trajectory Generation

Authors: Mayar Nour, Atrisha Sarkar, Mohamed H. Zaki

Organizations: Civil and Environmental Engineering, University of Western Ontario, London, Ontario, Canada · Electrical and Computer Engineering, University of Western Ontario, London, Ontario, Canada

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Abstract

Work zone navigation remains one of the most challenging manoeuvres for autonomous vehicles (AVs), where constrained geometries and unpredictable traffic patterns create a high-risk environment. Despite extensive research on AV trajectory planning, few studies address the decision-making required to navigate work zones safely. This paper proposes a novel game-theoretic framework for trajectory generation and control to enhance the safety of lane changes in a work zone environment. By modelling the lane change manoeuvre as a non-cooperative game between vehicles, we use a game-theoretic planner to generate trajectories that balance safety, progress, and traffic stability. The simulation results show that the proposed game-theoretic model reduces the frequency of conflicts by 35 percent and decreases the probability of high risk safety events compared to traditional vehicle behaviour planning models in safety-critical highway work-zone scenarios.

Figures & tables

Fig. 1 : SUMO representation of the 7 km M50 motorway segment in Dublin, Ireland, highlighting the work zone study area.
ParameterValue
Closed lanes1
Duration30 min
Time15:00-15:30
Step Length0.1 sec
Trajectory Planning6 sec
Re-planning Frequency2 sec
TABLE I : Simulation Parameters
ParametersAV L2AV L4
Car Following ModelC-ACCIDM
Speed deviation0.050.05
minGap1.51
tau τ\tau0.80.6
sigma σ\sigma0.050.05
Lane Change ModelSL2015SL2015
TABLE II : car following and Lane change parameters
Fig. 2 : The Game-Theoretic Framework architecture.
Fig. 3 : Fig a: Game-theoretic control reduces lateral conflict frequency under L2 automation across simulation seeds. Fig b: The 5th percentile of TTC values is higher under Game-theoretic control.
Fig. 4 : Under L2, the proposed framework shifts the TTC distribution toward higher values, reducing the probability of high-risk interactions ( TTC<2TTC<2 s) compared to the baseline scenario. Risk regions are defined as high risk ( <2<2 s), moderate risk (2–3 s), and safe ( >3>3 s).
Fig. 5 : Under L4, the proposed framework produces a slight rightward shift in the TTC distribution. Risk regions are defined as high risk ( <2<2 s), moderate risk (2–3 s), and safe ( >3>3 s).
AutomationMetricBaseline MeanGame Meanpp -value
L2Conflicts/min525.5342.90.005
L2TTC p05 (s)0.621.970.048
L2TTC << 2 s (%)27.976.180.0085
L4Conflicts/min288.4286.10.8209
L4TTC p05 (s)11.5116.050.15
L4TTC << 2 s (%)2.760.310.2746
TABLE III : Paired t-test results across matched simulation seeds (N = 10).
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