CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving
Score
72提出课程与奖励自适应联合机制，在长距离城市驾驶基准上带来显著提升。
实验设计完整，包含基线对比、消融与跨城镇泛化评估，结果支撑充分。
采用轻量状态表示和零样本迁移，对资源受限平台部署具有实用参考。
自动驾驶强化学习是活跃方向，但该预印本尚未经过同行评审，时效性适中。
来自arXiv AI/CL/LG预印本，可信度较好但未经正式同行评审。
Base 80.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The paper demonstrates significant performance gains and strong cross-town generalization in long-horizon autonomous driving, with complete experiments and clear ablations, giving it strong value and timeliness.
AI commentary
「The elegance of this work lies in scheduling both “what to learn” and “how to reward” simultaneously, rather than stacking network architecture. Teams working on autonomous driving or robot navigation should pay attention to its curriculum design and reward-weight scheduling, especially the generalization achieved with a low-dimensional state.」
Quick overview
CORAL proposes a curriculum-optimized reward adaptation method for LiDAR-based goal-directed urban driving. It jointly advances a five-stage curriculum that gradually extends routes and tightens behavioral constraints, and a stage-aware reward whose component weights shift from mission progress toward route following, safety, smoothness, and rule compliance. The policy is a multi-stream actor-critic network trained with PPO in CARLA on a compact 99-dimensional state consisting of a polar LiDAR histogram, vehicle telemetry, ego-frame route geometry, and traffic-rule indicators. Compared with two PPO baselines under an identical protocol, CORAL reaches the goal in all twenty evaluation episodes on the longest routes under full behavioral constraints, whereas the baselines reach 5% and 10%. Factorial ablations show that neither schedule alone matches their combination; removing either lowers success and route completion, and disabling both drops success to 55%. Trained in one town, the policy transfers zero-shot to seven unseen towns, succeeding in 68-98% of episodes, with mean lateral deviation below 0.35 m.
- Proposes CORAL, which jointly schedules a five-stage curriculum and stage-aware reward adaptation to address the learning-order problem of long-horizon driving behaviors under a fixed reward.
- Uses only a 99-dimensional state (polar LiDAR histogram + vehicle telemetry + ego-frame route geometry + traffic-rule indicators), requiring no point-cloud encoder or BEV rasterization.
- Achieves 100% success on the longest routes under full behavioral constraints, vs. 5% and 10% for two PPO baselines; ablations show neither mechanism alone matches their combination.
- After training in one town, zero-shot transfers to seven unseen towns with 68-98% success and mean lateral deviation below 0.35 m.
Full text
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