LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking (2501.08168v1)

Abstract: While autonomous driving technology has made remarkable strides, data-driven approaches still struggle with complex scenarios due to their limited reasoning capabilities. Meanwhile, knowledge-driven autonomous driving systems have evolved considerably with the popularization of visual LLMs. In this paper, we propose LeapVAD, a novel method based on cognitive perception and dual-process thinking. Our approach implements a human-attentional mechanism to identify and focus on critical traffic elements that influence driving decisions. By characterizing these objects through comprehensive attributes - including appearance, motion patterns, and associated risks - LeapVAD achieves more effective environmental representation and streamlines the decision-making process. Furthermore, LeapVAD incorporates an innovative dual-process decision-making module miming the human-driving learning process. The system consists of an Analytic Process (System-II) that accumulates driving experience through logical reasoning and a Heuristic Process (System-I) that refines this knowledge via fine-tuning and few-shot learning. LeapVAD also includes reflective mechanisms and a growing memory bank, enabling it to learn from past mistakes and continuously improve its performance in a closed-loop environment. To enhance efficiency, we develop a scene encoder network that generates compact scene representations for rapid retrieval of relevant driving experiences. Extensive evaluations conducted on two leading autonomous driving simulators, CARLA and DriveArena, demonstrate that LeapVAD achieves superior performance compared to camera-only approaches despite limited training data. Comprehensive ablation studies further emphasize its effectiveness in continuous learning and domain adaptation. Project page: https://pjlab-adg.github.io/LeapVAD/.

• The paper introduces LeapVAD, a novel system integrating dual-process thinking and cognitive perception that outperforms data-only approaches with a 5.3% improvement on short routes and 42.6% on long routes.
• The study details a dual-process module combining an analytic (System-II) and a heuristic (System-I) process to mirror human reasoning and adapt to complex driving scenarios.
• LeapVAD demonstrates strong generalization across simulators and continuous learning from past experiences, highlighting its potential for enhancing safety in autonomous driving.

A Detailed Examination of LeapVAD: Integrating Cognitive Perception and Dual-Process Thinking in Autonomous Driving

The paper presents LeapVAD, a sophisticated approach to autonomous driving that leverages cognitive perception alongside dual-process thinking to enhance the driving capabilities of autonomous systems. This method is particularly innovative as it integrates a human-attentional mechanism to enhance decision-making by focusing on critical traffic elements that bear influence on driving decisions. It represents an advancement over purely data-driven methodologies by incorporating elements of human cognitive processes.

Core Methodology and Innovative Features

LeapVAD distinguishes itself by integrating a dual-process decision-making module that mirrors human cognitive functions during driving. This system is composed of two processes:

1. Analytic Process (System-II): This process models human analytical reasoning. It extends driving experience through logical reasoning and stored knowledge, allowing it to adapt to novel and complex situations.
2. Heuristic Process (System-I): This component is akin to intuitive and fast responses in human drivers. It uses fine-tuning and few-shot learning to improve responses over time, aligned with how drivers develop muscle memory through experience.

Additionally, LeapVAD introduces a scene encoder network for rapid retrieval of relevant experiences, which compacts scene representations, facilitating more efficient decision-making processes. This system is especially noted for its ability to learn continuously from past experiences, thus improving its decision-making capabilities iteratively through a growing memory bank.

Numerical Performance and Evaluation

The efficacy of LeapVAD is measured extensively against benchmark autonomous driving simulators, such as CARLA and DriveArena. Results show that LeapVAD demonstrates improved performance compared to camera-only approaches and achieves these results with less training data. Specifically, the evaluations on CARLA Town benchmarks indicated a 5.3% improvement in driving score on short routes and a significant 42.6% gain on long routes, relative to previous models. Moreover, LeapVAD proves its adaptability by achieving commendable results in DriveArena using a memory bank developed through experiences in CARLA, illustrating strong generalization capabilities across different domains.

Implications and Theoretical Contributions

The integration of cognitive perception and dual-process thinking introduces a robust framework for advancing autonomous driving capabilities beyond current data-driven paradigms. By mimicking human cognitive processes, LeapVAD addresses complex, dynamic environments more effectively, offering significant contributions to both epistemic and practical dimensions of autonomous vehicle development. Practically, this approach can lead to more reliable and safer autonomous systems capable of self-improvement and adaptation to novel environments.

Theoretically, LeapVAD's design paves the way for future research into cognitive architectures in machine learning, particularly in domains requiring real-time decision-making and continuous learning. It reflects an important step toward achieving an overview between data-driven and knowledge-driven models, leveraging strengths from both methodologies.

Future Prospects

LeapVAD enriches the domain of autonomous driving with a framework that not only learns from data but also adapts knowledge akin to human learning processes. Its reflective mechanism and dual-process architecture could be extended to incorporate more sophisticated models of attention and reasoning, improving the robustness and reliability of autonomous systems further. In the broader AI landscape, similar frameworks can potentially be applied to other autonomous systems that require adaptive and context-aware decision-making.

In conclusion, LeapVAD represents a notable advancement in autonomous driving technology, raising important discussions around integrating cognitive inspiration into AI system designs, with promising avenues for further exploration and practical deployment in various dynamic and complex environments.