With the rapid development of science and technology, autonomous driving technology has gradually entered people's field of vision. In the past few years, companies such as Tesla, Waymo and Uber have attracted widespread attention for their investment and research and development in the field of autonomous driving. Although autonomous driving technology is expected to transform the transportation industry and bring many conveniences, there are still many key issues and challenges that need to be solved before it can be widely used. This article will focus on the issues and challenges in autonomous driving planning and control decision-making, analyze the current dilemmas faced, and put forward some constructive suggestions and solutions.

We will first provide an in-depth analysis of the current problems and challenges faced in formulating autonomous driving control strategies, such as model generalization, safety reliability, computing efficiency, etc. Finally, combined with advanced experience and practice at home and abroad, we will propose a series of possible solutions in order to provide a useful reference for the development and popularization of autonomous driving technology.

Through the elaboration of this article, we hope to improve people's understanding of the issues and challenges of autonomous driving regulation and control decision-making, and encourage the industry to strengthen cooperation and communication to jointly cope with the challenges brought by future autonomous driving technology, and bring safer and more efficient services to human society. , sustainable transportation mode.

01.

The importance of regulatory decisions

The importance of regulatory decision-making in the field of autonomous driving cannot be ignored, because it directly affects the success of the actual application of autonomous driving technology. First of all, regulatory decisions are crucial to ensuring the safety of autonomous vehicles. Through reasonable regulation, the incidence of traffic accidents can be effectively reduced and the safety of people's lives and property can be ensured. Secondly, efficient regulation and control decisions can help improve road traffic efficiency, alleviate traffic congestion, reduce energy consumption and exhaust emissions, thereby contributing to the realization of sustainable transportation development.

In addition, regulatory decisions also need to fully consider regulatory compliance, which means that the development of autonomous driving technology must be carried out within the legal framework to ensure road safety and safeguard public interests. Standardized regulatory decisions will help guide the development of autonomous driving technology in a more compliant and safe direction. At the same time, the public’s trust in autonomous driving technology is also a key factor in measuring the importance of regulatory decisions. Through transparent and reasonable regulation, the public's trust in autonomous driving technology can be strengthened and lay the foundation for its wider application.

To sum up, regulation and control decision-making plays a decisive role in the field of autonomous driving. It is related to the safety, efficiency, regulatory compliance and public acceptance of autonomous driving systems, and provides key support for the successful implementation and widespread application of autonomous driving technology. Therefore, in-depth study of regulatory and control decision-making issues and seeking effective solutions are important tasks to promote the healthy development of autonomous driving technology.

Issues and Challenges:

In the following articles, we will delve into the current problems and challenges faced by decision-making planning in the field of autonomous driving, as well as related potential solution directions and trends. We will focus on the following aspects:

1. Model generalization

2. Uncertainty estimation, data quality and quantity assessment

3. Multi-agent and agent-environment interaction

4.Safety and Reliability

5. Computational efficiency

6. Use multi-modal fusion for optimal decision-making

7. Interpretability and Explainability

8. Autonomous driving without HD maps

9. Integration with existing infrastructure

In the previous article, we focused on analyzing three aspects: model generalization, uncertainty estimation, and data quality and quantity assessment, as well as multi-agent and agent-environment interaction. In this article, we will continue to analyze the problems and challenges faced by decision planning in the field of autonomous driving, focusing on the three aspects of safety and reliability, computational efficiency, and the use of multi-modal fusion for optimal decision-making.

02.

Safety and reliability

Autonomous vehicles must be able to make safe, reliable and trustworthy decisions in a variety of complex and unpredictable scenarios [1] . These decisions must be made in real time, with limited information, and must take into account the behavior of other subjects on the road. This includes designing systems that can detect and recover from failures, as well as methods for testing and validating system performance in a variety of scenarios.

Autonomous vehicles must have the ability to make safe, stable and reliable decisions in various complex and uncertain scenarios. This means that in a real-time dynamic environment, autonomous vehicles need to make appropriate decisions quickly based on limited information conditions, and must fully consider the behavior of other driving subjects on the road to ensure driving safety. To achieve this goal, advanced algorithms and mechanisms need to be designed to prevent possible accidents. In addition, in order to continuously improve the performance of autonomous driving systems, researchers need to develop a comprehensive and rigorous testing and verification method to verify system performance in various actual scenarios. This includes extensive experiments in simulated environments, as well as real-vehicle testing under actual road conditions, to ensure that autonomous vehicles can perform well in different scenarios. In short, autonomous vehicles need to make safe, reliable and trustworthy decisions when facing complex and unpredictable scenarios. To achieve this goal, designing advanced algorithms and mechanisms, and developing rigorous testing and verification methods are key research directions.

Consider a cyclist [2]

sub-challenge

1. Verification and validation of autonomous vehicle decision-making : In a complex and ever-changing environment, it can be challenging to verify and validate autonomous vehicle decision-making behavior based on deep learning models. Due to the inherent complexity and opacity of deep learning models, traditional testing and verification methods are likely to be unable to fully guarantee the safety and reliability of autonomous vehicle decision-making.

2. Safety affected by external data : The performance and safety of autonomous vehicles largely depend on their ability to accurately sense the surrounding environment and process external data. However, these vehicles may encounter incomplete, inaccurate, or erroneous external data, which creates significant challenges for algorithm development. Therefore, how to develop robust and fault-tolerant algorithms so that autonomous vehicles can maintain safe driving under various abnormal conditions is an important direction of current research.

3. Safe decision-making in the presence of external model decisions in the cloud : In the presence of external model decisions in the cloud, how to ensure that autonomous vehicles make safe decisions is a very important issue. Cloud models can provide real-time traffic information, road conditions and other data for autonomous vehicles to help improve the accuracy and efficiency of decision-making. However, there are some challenges in ensuring safe decisions are made during this process. For example, communication delay and instability: When self-driving cars rely on cloud models to make decisions, communication delays and instability may affect the real-time nature of decisions; Data security and privacy protection: During the cloud decision-making process, self-driving cars need to communicate with the cloud The server performs data exchange, which involves data security and privacy protection issues; Robustness and fault tolerance: When autonomous vehicles rely on cloud models to make decisions, they must have the ability to cope with cloud service interruptions or other abnormal situations.

Potential solutions and trends

1. Combination of security rule methods and deep learning technology [3] ：这种结合策略旨在充分发挥两种方法各自的优势，为自动驾驶汽车提供更高的安全性能和决策能力。基于规则的方法具有较高的透明度和可解释性，使得研究人员和工程师能够更容易地理解和评估自动驾驶系统的决策过程。这有助于及时发现潜在的问题，提高系统的安全性和可靠性。而深度学习技术在处理复杂场景和动态环境方面具有很高的灵活性和适应性。通过结合基于规则的方法，自动驾驶汽车可以在遵循预设安全规则的前提下，灵活应对各种不确定和变化的道路条件，实现更加高效的决策过程。同时基于规则的方法可以为深度学习模型提供结构化的知识和先验约束，从而减少训练时间和提高决策效率。

InterFuser [3]

2.模型检查和定理证明，验证决策方法的正确性 ：利用形式化方法，如模型检查和定理证明，即便在存在不确定性和错误的情况下，也可以确保自动驾驶汽车在各种场景下的正确行为和决策。通过模型检查和定理证明等形式化方法，可以对自动驾驶汽车决策系统的正确性进行严格验证。这有助于在系统部署前发现并修复潜在的安全漏洞，从而确保其在实际应用中的安全性和可靠性。

3.对抗性训练和异常检测 ：为提高深度学习模型的鲁棒性，可以采用诸如对抗性训练和异常检测的技术。对抗性训练是一种训练策略，通过在对抗性示例上训练模型，提高模型在面对攻击或干扰时的稳定性和鲁棒性。这种方法有助于确保自动驾驶汽车在遇到极端或异常情况时仍能做出正确的决策。异常检测技术可以帮助深度学习模型识别出意外输入，并根据预设的安全策略作出适当的响应。这包括在检测到异常情况时激活安全措施，如减速、刹车或转向，以确保车辆和乘客的安全。

对抗训练例子 [4]

03.

计算效率

自动驾驶汽车必须能够在实时、有限计算资源的条件下做出决策。 为实现这一目标，我们需要开发能够在高效、迅速执行的同时，保持准确和可靠性的决策方法。

2.实时处理与模型推断，因为自动驾驶汽车必须在毫秒级时间尺度上做出决策 ：自动驾驶系统需要实时处理来自各种传感器的数据，并在极短的时间内做出正确决策。这对于那些可能需要大量计算时间的深度学习模型来说，是一个巨大的挑战。自动驾驶汽车在行驶过程中需要实时识别和响应各种复杂的道路条件、交通状况以及其他道路用户。为确保安全性和准确性，系统需要在毫秒级时间尺度内完成数据处理和决策推断。深度学习模型在处理大量参数和计算复杂度时，可能需要较长时间进行推断。这可能导致自动驾驶汽车在实时场景中无法满足决策速度的要求。

3.便携式NPU设备中的计算和内存资源限制 ：自动驾驶系统通常部署在资源受限的平台上，如嵌入式系统或移动设备，这可能限制可用的计算能力和内存。在便携式NPU设备上运行自动驾驶系统，需要在有限的计算资源和内存空间内完成各种任务，如图像识别、路径规划和控制。这些限制可能导致模型性能下降或响应速度减缓，从而影响整体系统的可靠性和安全性。便携式NPU设备的计算能力相对于高性能GPU或服务器来说较低，可能无法满足复杂深度学习模型的实时推断需求。同时，便携式NPU设备的内存容量有限，可能无法容纳大型深度学习模型。此外，内存带宽和访问速度的限制也可能导致模型推断速度降低。

潜在的解决方案和趋势

1. 剪枝、量化和知识蒸馏 ：开发优化深度学习模型的技术，如剪枝、量化和知识蒸馏，以减小模型的尺寸并提高其效率。这些技术有助于减少运行模型所需的计算量，使模型更适合实时处理。剪枝方法通过消除模型中不重要或冗余的参数（例如权重或神经元），来减少模型的计算量和内存需求 [5] 。常见的剪枝策略包括权重剪枝（移除较小权重）和结构化剪枝（移除整个神经元或通道）。这些策略可以在保持模型性能的同时，显著降低模型复杂度。量化方法通过减少表示模型参数所需的位数来降低模型尺寸。例如，将32位浮点数转换为16位或更低位数的整数。量化可以显著减小模型大小，降低内存占用和计算需求，同时仅引入较小的精度损失。知识蒸馏是一种模型压缩技术，通过将一个大型、复杂的“教师模型”所学到的知识传递给一个较小、简单的“学生模型”。这种方法通过让学生模型模拟教师模型的输出概率分布，从而使学生模型在保持较小尺寸的同时，获得与教师模型相近的性能 [6] [7] 。

知识蒸馏通用框架 [8]

2.  硬件加速 ：专用硬件，如图形处理器（GPU）、张量处理器（TPU）和现场可编程逻辑门阵列（FPGA），为深度学习模型提供了强大的计算加速能力。借助这些专用硬件，模型能够更高效地运行，延迟大幅降低，从而更适应实时处理场景。GPU：图形处理器（GPU）具有大量的并行处理单元，可同时执行多个计算任务。这使得GPU非常适合处理深度学习模型中的矩阵和张量运算。相较于传统的中央处理器（CPU），GPU能显著提高模型的计算速度和效率。张量处理器（TPU）是专为深度学习应用设计的定制硬件加速器。TPU专注于执行深度学习模型中的矩阵和向量运算，通常比GPU在性能和能效方面更具优势。TPU可以进一步提升模型的实时推断 速度，从而满足自动驾驶汽车的实时决策需求。现场可编程逻辑门阵列（FPGA）是一种可重新配置的硬件平台，可以根据特定应用需求定制硬件逻辑。FPGA在深度学习领域的优势在于其灵活性和低功耗。通过为特定模型定制硬件逻辑，FPGA可以实现高效的计算性能，同时降低能耗。

3.稀疏表示和神经网络结构搜索 ：稀疏表示是一种高效的数据表示方法，它通过仅使用少量非零元素来精确地表示数据。这种方法可以有效地压缩输入数据并降低模型所需的计算量。对于深度学习模型来说，稀疏表示可以应用于权重矩阵、激活矩阵或其他相关参数，从而提高计算效率并降低内存需求。神经网络结构搜索（Neural Architecture Search，NAS）是一种自动化技术，用于寻找最佳的神经网络结构和超参数组合。NAS的目标是在维持模型性能的同时，找到具有更高计算效率的网络结构。这些结构可能包括不同的层数、神经元数量、激活函数等。通过利用NAS，研究人员可以为自动驾驶汽车设计更高效且计算需求更低的深度学习模型。

04.

利用多模态融合进行最优决策

在自动驾驶领域中，实现多模态融合以制定最优决策是一项巨大的挑战。自动驾驶汽车必须具备根据来自各种传感器和信息源的数据进行决策的能力，这些传感器和信息源包括相机、激光雷达、雷达、GPS和地图等。然而，在实际应用中，这些不同的信息源可能会提供相互矛盾或不完整的信息，这进一步增加了确定最优决策的难度。

三种融合 [9]

子挑战

1.数据预处理以最大化多模态融合中的信息信号 ：在自动驾驶领域，收集的数据需要经过预处理过程来消除噪声、异常值和不一致性，从而确保决策的准确性和可靠性。然而，针对不同类型的数据进行有效预处理是一项颇具挑战性的任务，因为各种数据类型可能需要应用不同的预处理技术

2.来自多个传感器的多模态数据集成 ：多模态数据集成主要涉及将来自不同传感器和信息源（如相机、激光雷达、雷达、GPS和地图）的数据高效地整合在一起，同时要尽量减少噪声、冗余和不一致性。

3.从具有不同特征的多模态输入数据中提取特征 ：不同模态（如视觉、雷达和激光雷达等）的数据具有各自独特的特点，因此识别能够帮助自动驾驶系统做出最优决策的关键特征至关重要，挑战在于识别可用于做出最优决策的最相关特征。

4.设计模型以最大化融合特征对最终决策的贡献 ：设计一个能够处理多模态数据的深度学习模型是一项复杂的任务。这样的模型不仅需要处理各种不同类型的数据，还需要以最优方式融合它们。此外，为了适应可能未在训练数据中出现的新场景，模型必须具备一定的泛化能力。

潜在的解决方案和趋势

1.数据增强、过滤和归一化 ：研究人员可以使用高级数据预处理技术，如数据增强、过滤和归一化，以消除噪声和异常值。这有助于确保数据质量高，并可用于最优决策。数据增强技术通过对原始数据进行变换和扩展，以产生具有多样性和代表性的新数据 [10] 。这些变换包括旋转、缩放、翻转、平移等，能够增加模型的泛化能力，提高其在面对新场景时的性能。在自动驾驶领域，数据增强有助于模型更好地适应不同的道路条件、光线和天气状况。过滤技术可以去除数据中的噪声和异常值，使模型专注于学习有意义和关键的特征。在自动驾驶系统中，过滤可以通过传统的信号处理方法（如卡尔曼滤波器、中值滤波器等）或机器学习算法（如支持向量机、随机森林等）实现，从而提高模型的准确性和稳定性。归一化技术可以将来自不同传感器和数据源的数据统一到一个共同的尺度上，以消除数据分布的差异。这样可以简化模型的训练过程，加快收敛速度，并提高模型的可靠性。

数据增强例子 [11]

2.使用融合模型进行数据集成 ：融合模型旨在将多种数据源的信息融合到一个统一的表示中，以便为自动驾驶系统提供更准确和可靠的决策依据。这些模型可以是基于机器学习的方法（如多层感知器、支持向量机等）或深度学习的方法（如卷积神经网络、循环神经网络等）。通过对多种传感器（如相机、激光雷达、雷达和GPS）的数据进行融合，这些模型可以捕捉到更丰富和更具辨识度的环境特征从而优化决策。

3.基于领域特定知识或设计的物理知识引导深度网络进行特征提取 ：在提取特征时，一种有效的方法是利用深度学习模型自动从多模态数据中挖掘相关特征。此外，研究人员还可以结合领域特定知识和物理原理来引导深度学习网络的特征提取过程，从而更精准地识别对最优决策具有关键作用的特征。这种方法有助于提高自动驾驶系统在处理复杂场景时的准确性和鲁棒性。

references: