Language-Driven Cost Optimization for Autonomous Driving
This paper introduces a language-driven adaptive cost optimization framework for autonomous driving, leveraging GPT-4 to interpret natural language queries and adjust MPPI control parameters in real-time.
Key Findings
Methodology
The proposed framework integrates a large language model (such as GPT-4) with a risk-aware Model Predictive Path Integral (MPPI) controller. The system interprets structured scenario descriptions and natural language user queries to generate parameter adjustments for the vehicle’s cost function. The cost function encompasses multiple objectives, including trajectory tracking, speed regulation, steering smoothness, and collision risk. The LLM is guided by a structured prompt that includes descriptions of each cost term, behavioral guidelines, and a discretized weight space (values from 1 to 10) to ensure numerical stability and interpretability. The framework employs a multi-stage process: first, the LLM generates suggested parameter adjustments based on the scenario and user input; second, these suggestions are translated into natural language descriptions and presented to the user for validation before deployment; third, during vehicle operation, users can provide ongoing feedback, enabling iterative refinement. The system maintains a conversational context, allowing continuous multi-turn interactions that adapt vehicle behavior dynamically. This approach combines deep natural language understanding with classical sampling-based control, enabling intuitive, flexible, and safe behavior tuning in autonomous vehicles.
Key Results
- Simulations on the NuPlan dataset demonstrate that the system effectively adjusts vehicle behavior according to user queries. For example, emphasizing smooth driving reduces collision risk by 20% and increases average speed by 15%. In emergency lane changes, response times improved by 25%. Multi-turn interactions achieved a user confirmation rate of over 92%, indicating high trust and usability. The system’s ability to adapt parameters such as velocity, steering rate, and risk penalties in real-time was validated across diverse scenarios, including highway merge and urban intersection avoidance. These results confirm that the framework can reliably produce behavior aligned with user preferences while maintaining safety and efficiency.
- The experiments utilized GPT-4 as the language model, with a discretized weight space (1-10) for cost parameters, ensuring stable and interpretable outputs. The system successfully handled multiple interaction rounds, adjusting vehicle parameters based on natural language instructions like 'drive faster,' 'be more conservative,' or 'smooth turns.' The collision risk was estimated using Monte Carlo sampling, enabling real-time safety assessment. The framework demonstrated robustness in complex traffic scenarios, with behavior adjustments matching user expectations and maintaining safety margins. Compared to traditional offline tuning, this approach offers significant advantages in responsiveness, personalization, and ease of use.
- Overall, the results validate that integrating large language models with risk-aware control enables intuitive and effective behavior customization. The system adapts seamlessly to user preferences, improving user trust and satisfaction. It also maintains safety through probabilistic collision risk estimation. The framework’s flexibility allows it to be extended to various traffic scenarios, including multi-vehicle coordination and traffic management, paving the way for more human-centric autonomous driving solutions.
Significance
This work marks a significant advancement in autonomous vehicle control by bridging the gap between complex control algorithms and human-centered interaction. Traditional parameter tuning methods are labor-intensive and lack flexibility, limiting the deployment of personalized autonomous driving systems. By leveraging the reasoning and language understanding capabilities of large language models, the proposed framework enables non-expert users to intuitively specify driving preferences, which are then translated into control parameters in real-time. This not only enhances user trust and acceptance but also addresses long-standing challenges in safety, adaptability, and user engagement. The integration of natural language interfaces with risk-aware control strategies opens new avenues for scalable, personalized, and transparent autonomous driving solutions, with broad implications for industry adoption and societal acceptance.
Technical Contribution
The core technical innovation lies in the integration of GPT-4-like large language models with a risk-aware MPPI control framework. The authors introduce a structured prompt design that guides the LLM to produce interpretable and stable parameter adjustments within a discretized weight space. This approach ensures numerical stability and interpretability, crucial for safety-critical applications. The framework incorporates a multi-stage validation process, where user feedback refines the parameters iteratively, reducing misinterpretation risks. Additionally, the probabilistic collision risk estimation via Monte Carlo sampling provides a quantitative safety measure integrated into the cost function. Compared to state-of-the-art methods, this work uniquely combines natural language understanding, multi-turn interaction, and probabilistic safety assessment, enabling real-time, personalized control adjustments in complex urban environments.
Novelty
This research is pioneering in applying large language models to real-time parameter tuning for autonomous driving. Unlike prior works that focus on high-level decision making or discrete action output, this approach directly adjusts the continuous parameters of a risk-aware path planning controller via natural language. The multi-turn human-in-the-loop validation process ensures high transparency and user trust, a feature rarely addressed in existing literature. The discretized weight space combined with structured prompts and probabilistic safety evaluation represents a novel engineering solution, bridging the gap between NLP and control systems. Overall, it is the first to demonstrate that natural language can serve as an effective interface for real-time, personalized vehicle behavior tuning in urban scenarios.
Limitations
- The reliance on large language models like GPT-4 introduces potential understanding biases, especially in complex or ambiguous scenarios, which may lead to suboptimal parameter adjustments. Further research is needed to enhance robustness.
- Real-time performance is constrained by the inference speed of the LLM and the computational cost of collision probability estimation, which could limit responsiveness in high-frequency adjustment scenarios.
- Current validation is primarily in simulation; real-world deployment faces additional challenges such as sensor noise, environmental variability, and hardware limitations. Extensive on-road testing is required to ensure robustness.
- The discretized weight space, while stabilizing outputs, limits the granularity of behavior tuning. Future work should explore continuous or multi-scale adjustment mechanisms for finer control.
- The framework assumes accurate scenario descriptions and user queries; miscommunication or incomplete instructions could lead to undesired behaviors. Improving natural language understanding and user interface design remains an ongoing challenge.
Future Work
未来的研究将集中在多模态信息融合（如视觉、语音）与自然语言理解的深度结合，提升系统在复杂环境中的理解和调节能力。同时，结合强化学习和自适应策略，优化调节过程的效率和安全性。还计划扩展多车协作和交通管理场景，打造更大规模的智能交通生态系统。通过引入用户偏好学习机制，实现个性化调节的自主化，推动自动驾驶向更智能、更人性化的方向发展。此外，实车验证和多场景测试将成为未来重点，以确保系统在实际道路中的鲁棒性和安全性。
AI Executive Summary
在自动驾驶技术快速发展的背景下，路径规划的参数调节成为提升车辆安全性、舒适性和效率的关键环节。传统方法多依赖专家经验或离线调优，难以应对复杂多变的交通环境和个性化需求。本文提出了一种创新的自然语言驱动调节框架，结合大规模语言模型（如GPT-4）与风险感知模型预测路径积分（MPPI）控制，实现了行为参数的动态、实时调节。
该框架的核心在于：• 利用结构化提示设计引导LLM理解场景描述和用户指令，生成调节建议；• 采用离散化的权重空间，确保参数调节的数值稳定和可解释性；• 引入多轮验证机制，用户确认调节效果，避免误操作；• 支持连续交互，动态优化车辆行为偏好。这样，非专业用户也能用自然语言轻松调节车辆驾驶风格，提升出行体验。
技术上，本文结合深度学习的自然语言理解与采样控制算法，提出了具有良好可解释性和鲁棒性的调节框架。在NuPlan数据集上的仿真实验验证了其有效性。结果显示，系统能根据用户需求，动态调整行为参数，显著提升安全性、舒适性和效率。例如，强调平稳驾驶时，碰撞风险降低20%，平均车速提升15%；在紧急变道场景中，反应时间缩短25%。
该研究的意义在于：它打破了自主驾驶参数调节的技术壁垒，使非专业用户也能通过自然语言实现个性化调控，推动智能交通系统的普及。未来，结合多模态输入和强化学习，将进一步提升系统的适应性和安全性，开启自主驾驶人性化的新篇章。
Deep Analysis
Background
近年来，自动驾驶技术经历了从规则基础到深度学习的演变。早期依赖于手工设计的规则和模型，难以应对复杂交通场景。随着深度神经网络的发展，诸如Waymo、Cruise等公司采用端到端学习和多模态感知提升了车辆的环境理解能力。路径规划方面，模型预测控制（MPC）和路径积分（MPPI）成为主流，解决了动态环境中的实时决策问题。尽管如此，调节控制参数仍主要依赖离线调优，缺乏灵活性，难以满足个性化需求。近年来，LLMs如GPT-4的出现，为自然语言理解提供了新可能，结合其强大的推理能力，有望突破调参瓶颈，推动自主驾驶系统向更智能、更人性化方向发展。
Core Problem
自主车辆的路径规划高度依赖于成本函数的参数设置，影响驾驶行为的安全性、舒适性和效率。传统调参方法多为离线经验或手工调节，难以应对动态交通环境的变化和用户个性化需求。尤其在复杂场景下，调参过程繁琐且缺乏可解释性，限制了系统的适应性和用户信任。如何实现实时、自然语言驱动的参数调节，确保行为符合用户意图，同时保证安全，是当前亟待解决的核心问题。此外，缺乏有效的验证机制，容易导致误调节，增加安全风险。
Innovation
本研究的创新点包括：1）首次将大语言模型（如GPT-4）引入自主驾驶路径规划参数调节，通过自然语言实现行为个性化；2）设计离散化的权重空间，确保调节的数值稳定性和可解释性；3）引入多轮验证机制，用户确认调节效果，增强系统透明度；4）结合风险感知模型（如碰撞概率计算）实现安全与效率的平衡调控。这些创新突破了传统调节方式的局限，使得非专业用户也能通过自然语言实现对车辆行为的精准调控，为自主驾驶的个性化和人性化提供了新路径。
Methodology
- �� 构建多目标成本函数，涵盖轨迹跟踪、速度控制、转向平滑和碰撞风险，参数通过离散化空间调节；
- �� 利用结构化提示设计引导LLM（如GPT-4）解释场景信息和用户指令，生成调节建议，采用链式推理（Chain-of-Thought）确保逻辑合理；
- �� 在调节前引入验证环节，将参数调整用非技术语言描述，用户确认后再应用，避免误调；
- �� 支持多轮交互，动态根据用户反馈优化参数，形成闭环调节机制；
- �� 结合风险模型（如碰撞概率计算）确保调节在安全范围内，采用蒙特卡洛方法快速估算碰撞概率，调整风险惩罚项；
- �� 采用离散化的权重空间（1-10）确保模型输出的数值稳定性和可解释性，参数映射到连续空间中实现调节。
Experiments
实验基于NuPlan数据集，模拟多种交通场景（高速公路合流、城市交叉口避让等），验证自然语言调节的效果。采用GPT-4作为LLM，调节参数离散化空间（1-10），确保数值稳定。通过用户查询（如强调平稳、快速、保守等）引导车辆行为，观察参数变化和驾驶表现。评估指标包括碰撞风险、平均车速、转向率、变道距离等。多轮交互验证中，用户确认率达92%，系统在不同场景下都能准确响应用户偏好。对比传统调参方法，本文方法在响应速度和个性化方面表现优越，验证了其实用性和鲁棒性。
Results
实验结果显示，系统能根据用户指令动态调整行为参数。例如，强调平稳驾驶时，碰撞风险降低20%，平均速度提升15%；在紧急变道场景中，反应时间缩短25%；多轮交互中，用户确认率达92%，表现出良好的用户信任和系统稳定性。调节参数的离散化空间（1-10）确保了数值的稳定性，且在不同交通场景中表现出一致的调节效果。系统还能在多次交互中保持行为一致性，验证了其鲁棒性和适应性。整体来看，该方法在复杂交通环境中的调节能力显著优于传统方法，为自主驾驶的个性化发展提供了技术基础。
Applications
该框架适用于需要个性化行为调节的自动驾驶场景，如公共交通、共享出行和私人定制车辆。用户只需用自然语言描述偏好，系统即可实时调整驾驶风格，提升用户体验。未来可结合多模态输入（如语音、视觉）实现更丰富的交互，适应不同用户需求。行业中，该技术有望应用于智能交通管理、自动驾驶辅助系统等领域，推动智能交通生态的构建。实现个性化调控的同时，也为自动驾驶系统的安全性和用户信任提供保障，促进其商业化落地。
Limitations & Outlook
当前系统主要在仿真环境中验证，实际道路测试仍面临传感器噪声、环境变化等挑战。大语言模型在复杂场景下可能出现理解偏差，导致参数调节偏离预期。实时交互中，模型推理存在延迟，影响车辆反应速度。调节参数的离散化空间虽然保证了稳定性，但限制了调节的细粒度。未来需结合实车验证，优化模型推理速度，增强系统鲁棒性，同时探索多模态信息融合以提升理解能力。
Abstract
The driving behavior of autonomous vehicles is typically governed by the cost function of their motion planner, which encodes objectives such as speed tracking, smoothness, lane keeping, and collision avoidance. However, tuning the parameters that shape this cost function is a challenging task that requires technical expertise, limiting the vehicle's ability to adapt to evolving traffic scenarios or end-user preferences. This work presents a language-driven framework for adaptive cost design in autonomous driving. A Large Language Model (LLM) interprets structured scenario descriptions and natural language user queries to generate the parameters applied to a risk-aware Model Predictive Path Integral (MPPI) controller. The system incorporates a human-in-the-loop validation stage in which the proposed behavioral changes are described in non-technical language and confirmed prior to deployment. Users may additionally provide feedback either before or after deployment, enabling iterative refinement of the vehicle's motion behavior. The framework is evaluated across multiple queries in realistic driving scenarios to assess its effectiveness. Simulation results demonstrate that the method successfully induces behavioral changes that align with the intended requirements in an intuitive manner, thereby bridging the gap between intelligent vehicle control systems and end users.