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/sharmasushil/Navigating-Uncertainty-Trajectory-Prediction .

翻译：自动驾驶汽车需要精准可靠的短时轨迹预测以实现安全高效的行驶。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，而近期研究重点转向端到端数据驱动系统。此类模型的设计通常受限于数据集的可用性，且现有数据集多局限于通用场景。为解决这一局限，我们利用CARLA仿真器开发了一个用于短时轨迹预测任务的合成数据集。该数据集规模庞大，包含被视为复杂场景的情况——行人横穿马路、车辆超车——并由6000张透视图图像及每帧对应的IMU与里程计信息构成。此外，我们还开发了基于卷积神经网络(CNN)和长短期记忆网络(LSTM)的端到端短时轨迹预测模型。该模型能处理边缘案例，如在斑马线附近减速、遇行人横穿马路时停车，而无需对周围环境进行显式编码。为加速相关研究并协助同行，我们将数据集与模型公开发布至研究社区。本数据集可通过https://github.com/sharmasushil/Navigating-Uncertainty-Trajectory-Prediction 公开获取。