Generating Out-Of-Distribution Scenarios Using Language Models

The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ensure that these systems can navigate safely and effectively under unpredictable conditions. Addressing Out-Of-Distribution (OOD) driving scenarios is essential for enhancing safety, as OOD scenarios help validate the reliability of the models within the vehicle's autonomy stack. However, generating OOD scenarios is challenging due to their long-tailed distribution and rarity in urban driving dataset. Recently, Large Language Models (LLMs) have shown promise in autonomous driving, particularly for their zero-shot generalization and common-sense reasoning capabilities. In this paper, we leverage these LLM strengths to introduce a framework for generating diverse OOD driving scenarios. Our approach uses LLMs to construct a branching tree, where each branch represents a unique OOD scenario. These scenarios are then simulated in the CARLA simulator using an automated framework that aligns scene augmentation with the corresponding textual descriptions. We evaluate our framework through extensive simulations, and assess its performance via a diversity metric that measures the richness of the scenarios. Additionally, we introduce a new "OOD-ness" metric, which quantifies how much the generated scenarios deviate from typical urban driving conditions. Furthermore, we explore the capacity of modern Vision-Language Models (VLMs) to interpret and safely navigate through the simulated OOD scenarios. Our findings offer valuable insights into the reliability of language models in addressing OOD scenarios within the context of urban driving.

Probing Multimodal LLMs as World Models for Driving

We provide a sober look at the application of Multimodal Large Language Models (MLLMs) within the domain of autonomous driving and challenge/verify some common assumptions, focusing on their ability to reason and interpret dynamic driving scenarios through sequences of images/frames in a closed-loop control environment. Despite the significant advancements in MLLMs like GPT-4V, their performance in complex, dynamic driving environments remains largely untested and presents a wide area of exploration. We conduct a comprehensive experimental study to evaluate the capability of various MLLMs as world models for driving from the perspective of a fixed in-car camera. Our findings reveal that, while these models proficiently interpret individual images, they struggle significantly with synthesizing coherent narratives or logical sequences across frames depicting dynamic behavior. The experiments demonstrate considerable inaccuracies in predicting (i) basic vehicle dynamics (forward/backward, acceleration/deceleration, turning right or left), (ii) interactions with other road actors (e.g., identifying speeding cars or heavy traffic), (iii) trajectory planning, and (iv) open-set dynamic scene reasoning, suggesting biases in the models' training data. To enable this experimental study we introduce a specialized simulator, DriveSim, designed to generate diverse driving scenarios, providing a platform for evaluating MLLMs in the realms of driving. Additionally, we contribute the full open-source code and a new dataset, "Eval-LLM-Drive", for evaluating MLLMs in driving. Our results highlight a critical gap in the current capabilities of state-of-the-art MLLMs, underscoring the need for enhanced foundation models to improve their applicability in real-world dynamic environments.

Infrastructure-based End-to-End Learning and Prevention of Driver Failure

Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersection. In this work, we present FailureNet, a recurrent neural network trained end-to-end on trajectories of both nominal and reckless drivers in a scaled miniature city. FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers. FailureNet can accurately identify control failures, upstream perception errors, and speeding drivers, distinguishing them from nominal driving. The network is trained and deployed with autonomous vehicles in the MiniCity. Compared to speed or frequency-based predictors, FailureNet's recurrent neural network structure provides improved predictive power, yielding upwards of 84% accuracy when deployed on hardware.