To address the issues of local optima, path oscillations, and goal unreachability in traditional artificial potential field (APF) methods for multivehicle collaborative formation obstacle avoidance, this paper proposes an improved APF approach. By implementing four key enhancements—defining the minimum potential energy for gravitational fields, incorporating Euclidean distance into repulsive fields, constructing road boundary repulsive potential fields, and establishing nonlinear formation stabilization force potential fields—the dynamic equilibrium mechanism between attraction and repulsion forces is optimized, thereby improving formation obstacle avoidance capability and driving stability. Numerical simulations demonstrate that in triangular formation obstacle avoidance scenarios, the improved algorithm achieves a 37.7% reduction in arrival time (22.3 s), 23.2% shorter total path length (55.7 m), and 61.5% faster formation recovery (2.5 s), while eliminating local optima and reducing goal unreachability rate from 25% to 2%. Physical prototype experiments further validate the algorithm′s robustness in dynamic environments, showing rapid restoration of triangular formations post-obstacle avoidance. This method provides an efficient and stable solution for multi-vehicle collaborative obstacle avoidance, demonstrating significant application value for intelligent transportation systems.