Autonomous drifting is an advanced technique that enhances vehicle maneuverability beyond conventional driving limits. This survey provides a comprehensive, systematic review of autonomous drifting research published between 2005 and early 2025, analyzing approximately 80 peer-reviewed studies. We employed a modified PRISMA approach to categorize and evaluate research across two main methodological frameworks: dynamical model-based approaches and deep learning techniques. Our analysis reveals that while dynamical methods offer precise control when accurately modeled, they often struggle with generalization to unknown environments. In contrast, deep learning approaches demonstrate better adaptability but face challenges in safety verification and sample efficiency. We comprehensively examine experimental platforms used in the field—from high-fidelity simulators to full-scale vehicles—along with their sensor configurations and computational requirements. This review uniquely identifies critical research gaps, including real-time performance limitations, environmental generalization challenges, safety validation concerns, and integration issues with broader autonomous systems. Our findings suggest that hybrid approaches combining model-based knowledge with data-driven learning may offer the most promising path forward for robust autonomous drifting capabilities in diverse applications ranging from motorsports to emergency collision avoidance in production vehicles.

This letter investigates attitude estimation based on biquaternions (complex quaternions) for robotic applications utilizing a single-vector observation from sensors, such as accelerometer and magnetometer. In this work, we discover the evolution of novel form of quaternion, termed the biquaternion, where each component is a complex number instead of a real scalar, in the attitude approximation process. This biquaternion form arises from the intermediate solution of differential equations obtained from quaternion attitude dynamics. We study the evolution trajectories of biquaternions in the attitude estimation workspace, unveiling their inherent patterns and physical interpretations. Furthermore, we investigate the convergence performance of the biquaternion-based attitude estimator by tuning different parameters, demonstrating its potential superiority over traditional real quaternion estimators. The proposed biquaternion attitude estimation framework offers a unique perspective on attitude representation and opens up new avenues for enhancing estimation accuracy and robustness.

Autonomous vehicles face significant challenges in executing emergency obstacle avoidance maneuvers beyond conventional driving limits. Previous approaches, relying on vehicle dynamics modeling or simplified learning methods, often struggle with generalization to diverse scenarios. This paper presents a novel deep reinforcement learning-based Drift Obstacle Avoiding (DOA) system for extreme obstacle avoidance. Our system integrates a drift path planner based on cubic spline curves with a controller trained in a high-fidelity simulator using expert demonstrations and a Refined-exploration Soft Actor Critic (RSAC) algorithm. Utilizing only real-world accessible vehicle states, the system exhibits robust performance across various scenarios, vehicle types, and challenging road conditions.