11 accepted papers
Bird's eye view (BEV)-based 3D perception plays a crucial role in autonomous driving applications. The rise of large language models has spurred interest in BEV-based captioning to understand object behavior in the surrounding environment. However, existing approaches treat perception and captioning
Recent advances in multi-modal large language models (MLLMs) have demonstrated strong performance across various domains; however, their ability to comprehend driving scenes remains less proven. The complexity of driving scenarios, which includes multi-view information, poses significant challenges…
The rapid iteration of autonomous vehicle (AV) deployments leads to increasing needs for building realistic and scalable multi-agent traffic simulators for efficient evaluation. Recent advances in this area focus on closed-loop simulators that enable generating diverse and interactive scenarios. Thi
Personalized driving refers to an autonomous vehicle’s ability to adapt its driving behavior or control strategies to match individual users’ preferences and driving styles while maintaining safety and comfort standards. However, existing works either fail to capture every individual’s preference pr
Perception is a crucial component of autonomous driving systems. However, single-agent setups often face limitations due to sensor constraints, especially under challenging conditions like severe occlusion, adverse weather, and long-range object detection. Multi-agent collaborative perception (CP) o…
Autonomous driving (AD) has made significant strides in recent years. However existing frameworks struggle to interpret and execute spontaneous user instructions such as "overtake the car ahead." Large Language Models (LLMs) have demonstrated impressive reasoning capabilities showing potential to br…
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 (LL…
Vision-language generative AI has demonstrated remarkable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However current benchmark datasets lack multi-modal point cloud image and language data pairs. Recent approaches utilize visual…
Safety and robustness are crucial factors in developing trustworthy autonomous vehicles. One essential aspect of addressing these factors is to equip vehicles with the capability to predict future trajectories for all moving objects in the surroundings and quantify prediction uncertainties. In this…
In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven vehicles will share the road networks together. In such a mixed traffic environment, CAVs need to understand and predict maneuvers of surrounding vehicles for safer and more efficient interactions, especially when h…
Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving automation applications based on expert systems or prede-fined control strategies are not in line with individual human driver's…