Traditional autonomous driving systems have mainly focused on making driving decisions without human interaction, overlooking human-like decision-making and human preference required in complex traffic scenarios. To bridge this gap, we introduce a novel framework leveraging Large Language Models (LLMs) for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback. Our contributions include a GPT-4-based programming planner that integrates seamlessly with the existing CARLA simulator to understand traffic scenes and react to human instructions. Specifically, we build a human-guided learning pipeline that incorporates human driver feedback directly into the learning process and stores optimal driving programming policy using Retrieval Augmented Generation (RAG). Impressively, our programming planner, with only 50 saved code snippets, can match the performance of baseline extensively trained reinforcement learning (RL) models. Our paper highlights the potential of an LLM-powered shared-autonomy system, pushing the frontier of autonomous driving system development to be more interactive and intuitive.

Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding. However, many joint models still suffer from the robustness problem, especially on noisy inputs or rare/unseen events. To address this issue, we propose a Joint Adversarial Training (JAT) model to improve the robustness of joint intent detection and slot filling, which consists of two parts: (1) automatically generating joint adversarial examples to attack the joint model, and (2) training the model to defend against the joint adversarial examples so as to robustify the model on small perturbations. As the generated joint adversarial examples have different impacts on the intent detection and slot filling loss, we further propose a Balanced Joint Adversarial Training (BJAT) model that applies a balance factor as a regularization term to the final loss function, which yields a stable training procedure. Extensive experiments and analyses on the lightweight models show that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and slot filling. In addition, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets.