When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks

• URL: http://arxiv.org/abs/2510.14677v1
• Date: Thu, 16 Oct 2025 13:34:12 GMT
• Title: When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks
• Authors: Steffen Hagedorn, Luka Donkov, Aron Distelzweig, Alexandru P. Condurache,
• Abstract summary: Rule-based traffic agents hide planner deficiencies and bias rankings.<n>We integrate the state-of-the-art learned traffic agent model SMART into nuPlan.<n>Our analysis shows that IDM-based simulation overestimates planning performance.
• Score: 39.146761527401424
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Planner evaluation in closed-loop simulation often uses rule-based traffic agents, whose simplistic and passive behavior can hide planner deficiencies and bias rankings. Widely used IDM agents simply follow a lead vehicle and cannot react to vehicles in adjacent lanes, hindering tests of complex interaction capabilities. We address this issue by integrating the state-of-the-art learned traffic agent model SMART into nuPlan. Thus, we are the first to evaluate planners under more realistic conditions and quantify how conclusions shift when narrowing the sim-to-real gap. Our analysis covers 14 recent planners and established baselines and shows that IDM-based simulation overestimates planning performance: nearly all scores deteriorate. In contrast, many planners interact better than previously assumed and even improve in multi-lane, interaction-heavy scenarios like lane changes or turns. Methods trained in closed-loop demonstrate the best and most stable driving performance. However, when reaching their limits in augmented edge-case scenarios, all learned planners degrade abruptly, whereas rule-based planners maintain reasonable basic behavior. Based on our results, we suggest SMART-reactive simulation as a new standard closed-loop benchmark in nuPlan and release the SMART agents as a drop-in alternative to IDM at https://github.com/shgd95/InteractiveClosedLoop.

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