Recent advancements in autonomous vehicle technology are increasingly focused on enhancing safety and efficiency in real-world driving scenarios. The introduction of high-fidelity datasets, such as those capturing transitional autonomous vehicle lane-changing behaviors, is providing critical insights into vehicle interactions with human drivers, which can inform traffic management systems. Concurrently, innovative approaches like infrastructure-taught 3D perception leverage existing roadside units to reduce the need for extensive manual data annotation, streamlining the training process for perception systems. Additionally, the integration of satellite imagery for online HD map construction is addressing challenges related to depth perception and occlusion, thereby improving navigation accuracy. Collaborative frameworks, such as those enabling safe lane changes in congested traffic, are also gaining traction, balancing safety and efficiency. These developments suggest a shift towards more data-driven, collaborative, and context-aware systems that promise to enhance the operational capabilities of autonomous vehicles in complex urban environments.
Operating autonomous vehicles at the absolute limits of handling requires precise, real-time identification of highly non-linear tire dynamics. However, traditional online optimization methods suffer ...
To safely operate, an autonomous vehicle must know the future behavior of a potentially high number of interacting agents around it, a task often posed as multi-agent trajectory prediction. Many previ...
Building robust 3D perception for self-driving still relies heavily on large-scale data collection and manual annotation, yet this paradigm becomes impractical as deployment expands across diverse cit...
Lane changing in dense traffic is a significant challenge for Connected and Autonomous Vehicles (CAVs). Existing lane change controllers primarily either ensure safety or collaboratively improve traff...
Trajectory optimization is a central component of fast and efficient autonomous racing. However practical optimization pipelines remain highly sensitive to initialization and may converge slowly or to...
Collaborative perception (CP) is a promising paradigm for improving situational awareness in autonomous vehicles by overcoming the limitations of single-agent perception. However, most existing approa...
Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1-2 automation but short of full autonomy, are increasingly sharing the road with human-driven vehicles (HDVs). As these systems...
Pure Pursuit (PP) is widely used in autonomous racing for real-time path tracking due to its efficiency and geometric clarity, yet performance is highly sensitive to how key parameters-lookahead dista...
In the emerging mixed traffic environments, Connected and Autonomous Vehicles (CAVs) have to interact with surrounding human-driven vehicles (HDVs). This paper introduces MSH-MCCT (Multi-Source Human-...
Online high-definition (HD) map construction is an essential part of a safe and robust end-to-end autonomous driving (AD) pipeline. Onboard camera-based approaches suffer from limited depth perception...