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Beyond Planning: Differentiable Control-in-the-loop End-to-E

1 reports1 reporting sourcesUpdated 5 days ago

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Source roundup

Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving. This research introduces DIVA, a differentiable control-in-the-loop framework for end-to-end autonomous driving that integrates an identified vehicle model into the training loop. By enabling gradient flow through the co…

Generated from attributed reports · Updated 55 minutes ago

Event evidence and corrections

0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.

Reported quantity · driving score: 86.7 other · Basis not reported
Supporting report

“ss training-time distribution shift and deployment-time model mismatch respectively. On the closed-loop Bench2Drive benchmark, DIVA achieves a driving score of 86.7, surpassing the widely adopted state-of-the-art approach by 3.3 points while nearly halving mean jerk and reducing brake switching by 64%, and maintains superior path tracking accuracy under significant vehicle model mismatch at deployment.”

Exact source · revision 1

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Reported quantity · control-only cycle: 3.8 other · Basis not reported
Supporting report

“rception to vehicle states. A learning-based control decoder replaces the external controller and is supervised on the rolled-out vehicle states, bypassing the aliased control-input labels. A rate-consistent control-input parameterization reconciles the heterogeneous sampling rates and enables asynchronous execution with a 3.8 ms control-only cycle. On-policy re-identification and an online disturbance observer addre”

Exact source · revision 1

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10/14
  1. arXiv Robotics — research abstracts
    Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving

    This research introduces DIVA, a differentiable control-in-the-loop framework for end-to-end autonomous driving that integrates an identified vehicle model into the training loop. By enabling gradient flow through the control system, DIVA closes the gap between planning and control, achieving a driving score of 86.7 on the Bench2Drive benchmark, surpassing state-of-the-art methods with reduced jerk and brake switching.

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