Search Results (2)

This study explores the relationship between perceived quality and happiness among self-driving tourists, focusing on the impact of the self-driving journey and sightseeing stages on multi-stage satisfaction and happiness. An online survey was conducted, and a Structural Equation Model (SEM) of perceived quality, satisfaction, and happiness was constructed to test the hypotheses. The results indicate that overall satisfaction with the self-driving experience significantly affects tourists’ happiness, with the indirect effect of attraction satisfaction being particularly notable. Perceived quality indirectly influences happiness by enhancing satisfaction, with key factors including unique attractions, guide services, and innovative entertainment products. Additionally, the development of self-driving parking facilities, public information dissemination, road key nodes and scenery design, and vehicle intelligence levels are critical to enhancing tourists’ happiness. This study provides a theoretical basis for improving the overall tourism experience. Full article

The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers. Full article