Tackling Existence Probabilities of Objects with Motion Planning for Automated Urban Driving

Finding a Landing Site on an Urban Area: A Multi-Resolution Probabilistic Approach

This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirement of fast exploration and high resolution is solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of th…

Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving

Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving. First, the proposed system utilizes available contextual information, such as visible cars and pedestrians, to estimate pedestrian emergence probabilities in occluded regions. These probabilities are then used in a risk assessment framework, and incorporated into…

执行不确定性下的巡检规划

Inspection planning under execution uncertainty

针对城市环境中定位误差导致的执行不确定性影响巡检规划性能的问题，提出基于IRIS框架、结合蒙特卡洛采样的IRIS-U²算法，可提供覆盖率、路径长度和碰撞概率的统计保证，经桥梁巡检案例验证有效，且有界次优解可在保保证的同时减耗时。

Tackling Real-World Autonomous Driving using Deep Reinforcement Learning

In the typical autonomous driving stack, planning and control systems represent two of the most crucial components in which data retrieved by sensors and processed by perception algorithms are used to implement a safe and comfortable self-driving behavior. In particular, the planning module predicts the path the autonomous car should follow taking the correct high-level maneuver, while control systems perform a sequence of low-level actions, controlling steering angle, throttle and brake. In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that p…

自动驾驶中的信念空间残余风险：在定位不确定性下的应用

Belief-Space Residual Risk for Automated Driving under Localization Uncertainty

本文提出在信念空间中扩展残余风险度量，通过建模自我姿态不确定性作为高斯分布，以评估自动驾驶系统在复杂城市环境和恶劣天气下的安全性。

Semantic sensor fusion: from camera to sparse lidar information

To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high-level contextual understanding of the surroundings is necessary to plan and execute accurate driving maneuvers. This paper presents an approach to fuse different sensory information, Light Detection and Ranging (lidar) scans and camera images. The output of a convolutional neural network (CNN) is used as classifier to obtain the labels of the environment. The transference of semantic information between the labelled image and the lidar point cloud is p…