Research Intern: Closed-loop E2E Driving
Listed on 2026-09-27
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Research/Development
Research Scientist
Research Intern:
Closed-loop E2E Driving - Honda Research Institute USA Research Intern:
Closed-loop E2E Driving
Job Number: P25IN
T-57
Honda Research Institute USA (HRI-US) is seeking a talented and motivated PhD-level research intern to explore methods to improve closed-loop planning performance — spanning policy learning, trajectory generation, and closed-loop training/evaluation — and validate them in state-of-the-art simulation benchmark. This internship is ideal for PhD students with a strong publication record in deep learning and planning who are looking to apply their research to practical robotics and autonomous systems.
Mountain View, CA
Key Responsibilities
- Research and develop planning modules for E2E driving models, including trajectory scoring/selection, diffusion- or anchor-based trajectory generation, and multi-modal planning heads
- Design closed-loop training strategies (e.g., closed-loop fine-tuning, RL/IL hybrid post-training, world-model-based rollout training) to mitigate distribution shift and compounding errors
- Build and run closed-loop evaluation pipelines on benchmarks such as NAVSIM, Bench2
Drive, CARLA Leader board, or nuPlan; analyze failure modes and drive iterative improvement - Conduct ablations and large-scale experiments; profile and optimize training/inference throughput on multi-GPU clusters
- Collaborate with full-time researchers and engineers; present findings internally and contribute to publications and/or patents
Minimum Qualifications
- Currently enrolled in a PhD program in Robotics, Computer Science, Electrical Engineering, or a related field.
- Solid foundation in deep learning and strong proficiency in Python and PyTorch.
- Understanding of motion planning and/or policy learning fundamentals (imitation learning, reinforcement learning, or trajectory optimization).
- Experience training and debugging neural networks at scale.
- Ability to read, implement, and extend recent research papers independently.
Bonus Qualifications
- Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, CoRL, ICRA, RSS)
- Hands‑on experience with E2E driving frameworks (e.g., UniAD, VAD, PARA-Drive, Diffusion Drive, Hydra-MDP) or closed-loop benchmarks (NAVSIM, Bench2
Drive, CARLA, nuPlan) - Experience with generative planners (diffusion policies, flow matching), world models, or RL post‑training of driving policies
- Familiarity with simulation infrastructure, sensor simulation, or scenario generation
- Track record in autonomous driving challenges/leaderboards (e.g., CARLA Leader board, Waymo/nuPlan challenges)
- Strong software engineering practices (CUDA/TensorRT optimization, distributed training, CI)
Years of Work Experience Required 0
Desired Start Date 1/25/2027
Internship Duration 3 Months
Position Keywords E2E, Closed loop simulation, Autonomous Driving
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