Autonomous vehicles require accurate and reliable short-term trajectory predictions for safe and efficient driving. While most commercial automated vehicles currently use state machine-based algorithms for trajectory forecasting, recent efforts have focused on end-to-end data-driven systems. Often, the design of these models is limited by the availability of datasets, which are typically restricted to generic scenarios. To address this limitation, we have developed a synthetic dataset for short-term trajectory prediction tasks using the CARLA simulator. This dataset is extensive and incorporates what is considered complex scenarios - pedestrians crossing the road, vehicles overtaking - and comprises 6000 perspective view images with corresponding IMU and odometry information for each frame. Furthermore, an end-to-end short-term trajectory prediction model using convolutional neural networks (CNN) and long short-term memory (LSTM) networks has also been developed. This model can handle corner cases, such as slowing down near zebra crossings and stopping when pedestrians cross the road, without the need for explicit encoding of the surrounding environment. In an effort to accelerate this research and assist others, we are releasing our dataset and model to the research community. Our datasets are publicly available on https://github.com/navigatinguncertainty.

翻译：自动驾驶车辆需要准确可靠的短期轨迹预测以实现安全高效驾驶。目前大多数商用自动驾驶车辆采用基于状态机的算法进行轨迹预测，而近期的研究重点转向了端到端的数据驱动系统。这类模型的设计往往受限于数据集的可获得性，现有数据集通常仅涵盖通用场景。为解决这一局限，我们利用CARLA模拟器构建了一个用于短期轨迹预测任务的合成数据集。该数据集规模庞大，包含行人横穿马路、车辆超车等复杂场景，由6000张透视视角图像及其对应的每帧IMU和里程计信息组成。此外，我们还开发了一种基于卷积神经网络（CNN）和长短期记忆网络（LSTM）的端到端短期轨迹预测模型。该模型能够处理接近人行横道减速、遇行人横穿道路停车等边缘案例，而无需显式编码周围环境。为加速该领域研究并助力同行，我们将数据集和模型开源给研究社区。数据集公开访问地址为：https://github.com/navigatinguncertainty。