Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Peng, Zhenghao"'
A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-board evaluation and forecasting traffic agents motion for onboard plannin
Externí odkaz:
http://arxiv.org/abs/2409.18343
The rapid development of artificial intelligence (AI) has unearthed the potential to assist humans in controlling advanced technologies. Shared autonomy (SA) facilitates control by combining inputs from a human pilot and an AI copilot. In prior SA st
Externí odkaz:
http://arxiv.org/abs/2409.15317
Autor:
Zhou, Yunsong, Simon, Michael, Peng, Zhenghao, Mo, Sicheng, Zhu, Hongzi, Guo, Minyi, Zhou, Bolei
Controllable synthetic data generation can substantially lower the annotation cost of training data. Prior works use diffusion models to generate driving images conditioned on the 3D object layout. However, those models are trained on small-scale dat
Externí odkaz:
http://arxiv.org/abs/2406.09386
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a Closed-loop Adversarial Training (CAT) framework for safe end-to-end
Externí odkaz:
http://arxiv.org/abs/2310.12432
Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and trajectory forecasting. Since the driving logs in these datasets conta
Externí odkaz:
http://arxiv.org/abs/2306.12241
The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrations. Assuming optimal, the teacher policy has the perfect timing and c
Externí odkaz:
http://arxiv.org/abs/2303.01728
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called TrafficGen for traffic scenario generation. It learns from the fragmented h
Externí odkaz:
http://arxiv.org/abs/2210.06609
Human-AI shared control allows human to interact and collaborate with AI to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempt the goal-conditioned design to achieve human-controllable policies at t
Externí odkaz:
http://arxiv.org/abs/2206.00152
Deep visuomotor policy learning, which aims to map raw visual observation to action, achieves promising results in control tasks such as robotic manipulation and autonomous driving. However, it requires a huge number of online interactions with the t
Externí odkaz:
http://arxiv.org/abs/2204.02393
Autor:
Huang, Mingxin, Liu, Yuliang, Peng, Zhenghao, Liu, Chongyu, Lin, Dahua, Zhu, Shenggao, Yuan, Nicholas, Ding, Kai, Jin, Lianwen
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection an
Externí odkaz:
http://arxiv.org/abs/2203.10209