The development of autonomous vehicles has been accelerating, marked by a variety of competitions that challenge teams with diverse missions. Recently, racing-based autonomous driving competitions have gained prominence. Notably, the 2023 Hyundai Motor Group Autonomous Driving Challenge (HMG ADC) stands out as a manufacturer-operated event with a racing concept. This competition was distinctive, featuring mass-produced vehicles on race track with multiple vehicles simultaneously. In this paper, we explore the AutoKU team’s participation in the HMG ADC, highlighting their system, which is designed for two types of driving: solo and multi-vehicle racing. We detail the use of an identical mass-produced Hyundai IONIQ 5 vehicle equipped for autonomous driving without any performance modifications. The paper will discuss AutoKU’s approach and performance in solo and multi-vehicle races, showcasing their strategies and achievements in this innovative autonomous racing challenge. (Video: https://youtu.be/wLtmUkahnYA?si=AjqH6hYe10O94laq).

In the realm of autonomous driving, motion planning for the ego vehicle necessitates the prediction of surrounding vehicles’ motions. This prediction traditionally relies on object tracking modules containing several sensors to gauge vehicle positions and velocities. However, existing physical or maneuver-based model approaches overlook important aspects of vehicle interactions that significantly affect actual vehicle movement. Ignoring these interactions can lead to inadequate ego vehicle’s motion planning. Addressing this gap, this paper proposes a novel approach: the Collision Probability Field (CPF)-based interaction-aware longitudinal motion prediction. Our methodology uniquely integrates the CPF, derived from the uncertainty of sensing information, to account for the probabilistic state of vehicle positions and velocities. This allows the prediction algorithm to consider not just the static data, but also the dynamic interactions between vehicles such as collision. Our approach was tested in various scenarios, including lane changes with an approaching vehicle from behind and different driver behavior models in real-world conditions. Our findings demonstrate a significant improvement in prediction accuracy for the motion planning of ego vehicle, highlighting the importance of interaction-aware predictions in autonomous driving systems.

Representing wild sounds as images is an important but challenging task due to the lack of paired datasets between sound and images and the significant differences in the characteristics of these two modalities. Previous studies have focused on generating images from sound in limited categories or music. In this paper, we propose a novel approach to generate images from in-the-wild sounds. First, we convert sound into text using audio captioning. Second, we propose audio attention and sentence attention to represent the rich characteristics of sound and visualize the sound. Lastly, we propose a direct sound optimization with CLIPscore and AudioCLIP and generate images with a diffusion-based model. In experiments, it shows that our model is able to generate high quality images from wild sounds and outperforms baselines in both quantitative and qualitative evaluations on wild audio datasets.