Computer Vision Navigation for an Autonomous Vehicle Model Using Reinforcement Learning
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2606. 03963v1 Announce Type: cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.
The paper introduces a low‑cost, open experimental platform for end‑to‑end autonomous driving on miniature Ackermann vehicles, combining a physical car, printed track, data collection tools, trajectory registration, and a Webots digital twin. It implements command‑conditioned behavior cloning, achieving a mean cross‑track error of 6.1 cm on the real vehicle and demonstrating the impact of camera field of view in simulation. Using synthetic data from the digital twin and a sim‑to‑real image translator, a higher‑capacity policy trained on both synthetic and real data completes all four track routes, outperforming the baseline trained only on real data.
arXiv:2606. 03963v2 Announce Type: replace-cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.
The paper introduces a low‑cost, open experimental platform for end‑to‑end autonomous driving on miniature Ackermann vehicles. It combines a physical vehicle, a printed urban track, data collection tools, trajectory registration, and a Webots digital twin to link simulation and real‑world experiments. Using command‑conditioned behavior cloning, the authors demonstrate that a neural policy can follow lanes with a mean cross‑track error of 6.1 cm, and that synthetic data plus a sim‑to‑real image translator improves performance on all track routes.
MILER is an end‑to‑end reinforcement learning framework that achieves zero‑shot sim‑to‑real transfer for autonomous driving in unstructured environments. It uses a custom semantic mid‑level representation (MLR) simulator for offline training, and during deployment it processes real camera and LiDAR data with BEVFusion to produce a compatible bird’s‑eye‑view representation. The policy’s actions are applied via a trajectory‑alignment strategy, allowing the system to drive 17.3 km on a 3.0 km test track without human intervention, all running on a Jetson AGX Orin.
arXiv:2607. 18637v1 Announce Type: cross Abstract: Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions.