目的 无人驾驶规划与控制是保障行驶安全的重要环节之一，现有的规划方法大多假定驾驶场景是精确感知的，忽略了行驶环境中存在的不确定性，如传感器数据不确定性、感知周围物体不确定性、系统运行不确定性等，忽略这些不确定性的影响将影响驾驶的安全。本文在考虑传感器数据不确定性的情况下，将系统中实际存在的定位不确定性融入规划系统,从而规划出更加安全可行的轨迹。方法 在定位不确定性的场景下通过研究基于栅格地图的不确定环境概率模型框架以及基于该表征框架的轨迹规划方法降低不确定性的影响，产生舒适安全的类人轨迹。该方法首先将先验地图转换为栅格地图作为全局栅格地图，接着结合定位系统将局部栅格地图初始化，然后在局部栅格地图中进行定位不确定性传播，最后在frenet坐标系进行轨迹规划，使用局部栅格地图的占据概率计算候选轨迹代价，选择最优代价轨迹。结果 本文方法在CARLA仿真器里面进行验证，利用ROS机器人操作系统进行系统设计，分别有地图构建、路径规划、场景生成等模块，通过仿真实验对比多种方法，验证了本文方法能够在定位不确定性环境下平稳行驶，安全的避开障碍物，在路径安全性和高效性上找到一个平衡点，在多种场景本文考虑定位不确定性的方法通过率提高15%。结论 本文提出了一种能够融入多种不确定性的环境表征框架，并将定位系统不确定性融入进去，提出基于frenet的考虑不确定性的规划方法，将碰撞检测这一环节通过代价函数优化，提高了规划的安全性和效率。

Objective Currently, autonomous driving planning methods assume that the information obtained is certain, but the actual situation contains a variety of uncertainties, ignoring these uncertainties may lead to safety problems. The measurements provided by the sensors generally include the value of the state and the uncertainty of the state value, which is usually represented by the covariance matrix. At present, most planning methods do not make good use of this uncertainty, and choose to ignore it directly. These uncertainties will also have an impact on planning, and when accumulated over time, the impact may even be large enough to cause serious accidents. If there is a method or framework that can deal with this uncertainty, it can provide more reference information for the planning system, which will have a positive effect on driving safety. The purpose of this paper is to address a critical challenge in the field of autonomous driving: the effective consideration of uncertainty, particularly focusing on sensor measurement errors. In the complex and dynamic environment of autonomous driving, uncertainties can arise from various sources, posing significant hurdles to accurate and safe planning. By concentrating on sensor uncertainty, the study delves deep into understanding the intricacies of sensor measurement errors, which are vital components of perception systems in autonomous vehicles. The primary objective is to develop robust planning algorithms that can account for these uncertainties, ensuring that autonomous vehicles can make informed decisions even when sensor data is imperfect or noisy. This research is pivotal for enhancing the reliability and safety of autonomous driving systems, ultimately paving the way for the widespread adoption of autonomous vehicles by addressing one of the key challenges in their deployment. Method The methodology in this article revolves around the innovative use of the grid map, a mathematical framework employed to characterize uncertainty as the occupancy probability within a grid map. The process begins by transforming the prior map into a grid map, establishing a global reference. Subsequently, the local grid map is initialized through integration with the localization system, enabling the propagation of localization uncertainty within this localized context. By integrating both localization and sensing uncertainties into the grid graph, the computational complexity of the planning algorithm is significantly reduced. This integration not only streamlines the planning process but also ensures the efficient and safe generation of optimal trajectories. Path planning is executed within the frenet coordinate system, where the occupancy probability of the grid map plays a pivotal role. This comprehensive methodology not only accounts for sensor measurement errors but also provides a systematic framework for path planning, ultimately enhancing the reliability and safety of autonomous vehicles in uncertain driving environments. Result The research findings, validated through extensive simulation tests in the CARLA simulator, confirm the robustness of the proposed methodology. The integration of the approach with the ROS-based system design, encompassing critical modules such as the map engine, path planning, and scene generation, proved pivotal in achieving these results. Specifically, the experiments demonstrated that considering localization uncertainty led to a significant improvement in the success rate across a range of obstacle avoidance scenarios. The proposed method exhibited exceptional performance, enabling smooth long-distance driving even in environments characterized by positioning uncertainty. It not only ensured the safe navigation of the vehicle, effectively avoiding obstacles, but also achieved an impressive average arrival rate of over 90% in diverse obstacle avoidance scenarios. These compelling outcomes underscore the effectiveness and reliability of the developed approach, positioning it as a promising solution for enhancing the safety and efficiency of autonomous vehicles in real-world driving conditions. Conclusion This research addresses a fundamental challenge in autonomous driving by focusing on the critical aspect of sensor measurement errors, particularly localization uncertainty. The study introduces an innovative grid-map-based planning method, implemented and rigorously tested within the CARLA simulator using the ROS framework. Through meticulous experimentation, the research demonstrates that considering localization uncertainty significantly improves the success rate in various obstacle avoidance scenarios. The proposed approach enables smooth, long-distance driving under positioning uncertainty, ensuring safe obstacle avoidance and achieving an outstanding average arrival rate of over 90% in diverse scenarios. These results highlight the efficacy and practicality of the developed methodology, showcasing its potential for real-world application in autonomous driving systems. By seamlessly integrating localization and sensing uncertainties into the planning algorithm, this research not only enhances the reliability and safety of autonomous vehicles but also contributes valuable insights into the ongoing efforts to make autonomous driving a mainstream reality. Future research can further improve the robustness and adaptability of autonomous driving systems by combining the grid-map-based representation framework with the uncertainty of other sensors such as LiDAR and cameras. This comprehensive approach can provide a more comprehensive and accurate environmental perception for the system, thereby increasing confidence in decision-making. By introducing the uncertainty of multiple sensors, researchers can better simulate and understand complex situations in actual driving environments, providing a more reliable foundation for safe navigation and intelligent decision-making of autonomous vehicles. This scalable research path will have significant implications for future research on autonomous driving, providing new directions for promoting innovation and development in this field.