This　article　preprocesses　environmental　information　and　use　it　as　input　for　the　Proximal　Policy　Optimization　(PPO)　algorithm.　The　algorithm　is　directly　trained　on　a　model　vehicle　in　a　real　environment,　allowing　it　to　control　the　distance　between　the　vehicle　and　surrounding　objects.　The　training　converges　after　approximately　200　episodes,　demonstrating　the　PPO　algorithm＇s　ability　to　tolerate　uncertainty,　noise,　and　interference　in　a　real　training　environment　to　some　extent.　Furthermore,　tests　of　the　trained　model　in　different　scenarios　reveal　that　even　when　the　input　information　is　processed　and　does　not　provide　a　comprehensive　view　of　the　environment,　the　PPO　algorithm　can　still　effectively　achieve　control　objectives　and　accomplish　challenging　tasks.　©　2024　Joint　Journal　of　Novel　Carbon　Resource　Sciences　and　Green　Asia　Strategy.　All　rights　reserved.