Enterprise AI Analysis
Sequence of Expert: Boosting Imitation Planners for Autonomous Driving Through Temporal Alternation
Imitation Learning (IL) in autonomous driving consistently faces the challenge of error accumulation in closed-loop systems, degrading reliability and safety. Traditional approaches often demand increased model complexity or extensive data.
This paper introduces the Sequence of Expert (SoE), a novel temporal alternation policy designed to enhance closed-loop performance without increasing model size or data requirements. SoE leverages the inherent differences in driving errors among models trained with different random seeds, alternating their activation at temporal intervals.
The impact is significant: SoE provides a plug-and-play solution that consistently and significantly improves the performance of all evaluated IL planners, achieving state-of-the-art results on large-scale autonomous driving benchmarks like nuPlan, all without additional computational overhead at test time.
Executive Impact & Key Metrics
SoE provides a pragmatic, high-impact solution for enhancing the reliability and safety of autonomous driving systems built on imitation learning, directly addressing critical deployment challenges.
0.0%
Avg. CL-NR Performance Improvement
SOTA
Closed-Loop Performance (nuPlan)
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Additional Inference Latency
Plug & Play
Integration Effort
Deep Analysis & Enterprise Applications
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Imitation Learning: Bridging the Reality Gap
Imitation learning (IL) faces critical challenges in autonomous driving, primarily error accumulation in closed-loop environments. Small, imperceptible errors compound over time, leading to distribution shift and system failures like collisions. Key observed limitations include:
- OL-CL Mismatch: The best open-loop imitation accuracy often does not translate to the best closed-loop performance.
- Early-Stage CL Superiority: Highest closed-loop scores frequently emerge from earlier training epochs, not the final converged models.
- Inter-Run Complementarity: Models trained with identical architecture and data but different random seeds exhibit substantial variance in closed-loop performance, indicating an exploitable source of diversity.
These issues highlight the inherent limitations of relying on a single model for robust autonomous driving in complex, real-world scenarios.
Sequence of Expert (SoE): Temporal Alternation Strategy
SoE addresses IL limitations by temporally alternating between multiple 'expert' policies. Instead of a single model, SoE leverages the intrinsic differences in error accumulation among models. The key mechanisms involve:
- Expert Selection: Utilizing models trained with the same architecture and data but different random seeds, exploiting their 'inter-run complementarity' to provide diverse error-handling capabilities.
- Temporal Scheduling: A simple, periodic alternation between two selected experts (e.g., policy A for
n-1steps, then policy B for 1 step, withn=2being empirically optimal). This allows different experts to 'take over' when one drifts towards failure states, effectively interrupting error accumulation.
This plug-and-play solution requires no architectural modifications or additional data, making it highly practical for real-world deployment.
Achieving SOTA & Enhanced Robustness with SoE
The SoE framework demonstrates consistent and significant improvements in closed-loop driving performance across various IL planners on large-scale benchmarks like nuPlan. It achieves state-of-the-art performance with a baseline planner, often with lower inference latency and training cost than more complex models.
- Significant Performance Boost: Achieved an average CL-NR improvement of 0.85% for Pluto w/o, and pushed Pluto-SoE to state-of-the-art.
- Reduced Performance Variance: While increasing the number of experts beyond two doesn't necessarily raise the upper performance bound, it significantly reduces performance variance among different SoE policies, enhancing overall stability.
- Efficiency: No additional computational overhead at test time and does not increase model size or data requirements.
This approach effectively transforms training variance from a source of noise into a valuable source of diversity for more robust autonomous driving.
0.85%
Average CL-NR Performance Improvement on Pluto w/o
Enterprise Process Flow
Train Multiple Models (M runs, different seeds)
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Select Best Checkpoints (Validation Set)
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Define SoE Policy (π_a, π_b from M)
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Temporal Alternation (periodic scheduling)
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Enhanced SoE Policy (π_SoE)
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Preventing Collisions: A planTF Example
Figure 2 in the paper vividly illustrates SoE's impact on a planTF driving log. The original policy (πa) led the ego vehicle to crash into a parked vehicle. By contrast, when SoE was applied (using πa and πb with a period n=2), πb intervened periodically.
At frame 51, where πa continued to neglect the parked vehicle, πb generated a crucial stopping trajectory. This timely temporal switch by SoE effectively interrupted the error accumulation of πa, demonstrating its ability to leverage complementary expert behavior. This intervention prevented the collision that a single policy would have otherwise caused, showcasing SoE's critical role in enhancing safety and reliability in autonomous driving scenarios.
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