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Efficient and Safe Planner for Automated Driving on Ramps Considering Unsatisfication

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arxiv 2504.15320 v1 pith:4XFC7AOC submitted 2025-04-20 cs.RO cs.SYeess.SY

Efficient and Safe Planner for Automated Driving on Ramps Considering Unsatisfication

classification cs.RO cs.SYeess.SY
keywords plannerautomatedefficiencylane-changingrampssafearrow-cluster-basedcurve
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Automated driving on ramps presents significant challenges due to the need to balance both safety and efficiency during lane changes. This paper proposes an integrated planner for automated vehicles (AVs) on ramps, utilizing an unsatisfactory level metric for efficiency and arrow-cluster-based sampling for safety. The planner identifies optimal times for the AV to change lanes, taking into account the vehicle's velocity as a key factor in efficiency. Additionally, the integrated planner employs arrow-cluster-based sampling to evaluate collision risks and select an optimal lane-changing curve. Extensive simulations were conducted in a ramp scenario to verify the planner's efficient and safe performance. The results demonstrate that the proposed planner can effectively select an appropriate lane-changing time point and a safe lane-changing curve for AVs, without incurring any collisions during the maneuver.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields

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    An autonomous driving planner that merges mean-field game interactions and potential fields into one unified field, claiming Nash equilibrium convergence but providing an incomplete proof and limited simulation evidence.

  2. Attention and Risk-Aware Decision Framework for Safe Autonomous Driving

    cs.RO 2025-09 reject novelty 4.0

    An improved PPO for highway driving uses a hybrid risk field, attention modules, a balanced reward, and a rule-based safety filter to reduce collisions and speed up training in simulation.

  3. Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving

    cs.RO 2025-09 reject novelty 4.0

    An adaptive risk-field-plus-MPC controller with a sigmoid evolution factor and TTC/TWH-based risk ellipses is claimed to achieve collision-free overtaking and lane changes in simulation.

  4. Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States

    cs.CV 2025-09 reject novelty 3.0

    CMLReID uses dynamic text prompts and dual-path prototypes to improve lifelong person re-identification in hybrid clothing states, reporting gains of about 5 mAP over four baselines.

  5. Scenario-based Decision-making Using Game Theory for Interactive Autonomous Driving: A Survey

    cs.RO 2025-09 reject novelty 1.0

    A scenario-based survey of game-theoretic autonomous driving decision-making that claims comprehensiveness but is undermined by a non-systematic methodology and numerous internal errors.