Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.