Decision-making and planning for autonomous vehicles on foggy highway obstacle sections under visual perception
Year: 2026
Type: article
Abstract: To enhance the safety and decision reliability of automated driving vehicles on foggy highway obstacle sections, this study addresses three key challenges: visual perception degradation, increased uncertainty in obstacle localization, and trajectory instability under high-speed conditions. A decision-making and planning method specifically designed for foggy scenarios is proposed. The method constructs a visual perception degradation model grounded in atmospheric scattering theory, and introduces uncertainty-inflated occupancy sets alongside blind-zone virtual occupancy sets to impose unified constraints on both visible and unobservable regions. An adaptive triggering mechanism is developed based on fog-effective time-to-collision, candidate behaviors are screened using velocity-adaptive terminal-state sampling, and trajectory optimization with collision avoidance control is accomplished via variable-step nonlinear model predictive control (NMPC) framework. The proposed method is validated across three dimensions: varying vehicle speeds, fog intensity levels, and representative obstacle scenarios. Results demonstrate that the method achieves stable, conservative, and executable closed-loop decision-making and planning under perception-limited conditions, simultaneously improving collision avoidance success rate, trajectory smoothness, and state convergence while maintaining real-time performance.
Authors: Shengchun Sui, Yongming He, Xinran Li, Bin Ran
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,040
Awards: G4458586591, G7033491786
OpenAlex ID: W7221165852