We present a novel sensor-based learning navigation algorithm to compute a collision-free
trajectory for a robot in dense and dynamic environments with moving obstacles or targets …

Trajectory prediction is one of the key components of the autonomous driving software stack.
Accurate prediction for the future movement of surrounding traffic participants is an important …

As the complexity of autonomous vehicles (AVs) continues to increase and artificial
intelligence algorithms are becoming increasingly ubiquitous, a novel safety concern known …

随着深度神经网络技术的快速发展, 大数据的涌现和计算能力的显著提升,
深度神经网络被越来越多地应用到各个安全攸关领域, 例如自动驾驶, 人脸识别 …

Machine learning (ML) has significantly enhanced the abilities of robots, enabling them to
perform a wide range of tasks in human environments and adapt to our uncertain real world …

Accurate trajectory prediction is vital for various applications, including autonomous
vehicles. However, the complexity and limited transparency of many prediction algorithms …

随着人工智能技术的深入发展, 自动驾驶已成为人工智能技术的典型应用,
近十年来得到了长足的发展, 作为一类非确定性系统, 自动驾驶车辆的质量和安全性得到越来越 …

Out-of-distribution (OOD) detection is essential for the reliability of ML models. Most existing
methods for OOD detection learn a fixed decision criterion from a given in-distribution …

In robotics, deep learning (DL) methods are used more and more widely, but their general
inability to provide reliable confidence estimates will ultimately lead to fragile and unreliable …

Reliable perception and detection of objects are one of the fundamental aspects of vehicle
autonomy. Although model-based approaches perform well in the area of planning and …