基于Qwen3-VL的自动驾驶场景实测

Core Insights - The article discusses the potential of multimodal large models in the autonomous driving sector, particularly focusing on Alibaba's Qwen3-VL model, which demonstrates strong capabilities in scene understanding, spatial reasoning, behavior judgment, and risk prediction [2]. Scene Understanding and Spatial Reasoning - The Qwen3-VL model was tested on various scenarios, showcasing its ability to describe images, assess weather conditions, identify road types, and detect pedestrians or vehicles [5][7][10][11]. - The model can analyze complex traffic situations, such as determining the closest vehicle and its movement status, as well as the intentions of vehicles in adjacent lanes [21][22][23][25][26]. Behavior Decision-Making and Causal Reasoning - The model can evaluate whether the vehicle should accelerate, decelerate, or maintain speed based on current conditions, and identify potential dangers in the environment [28][29][30]. - It can also interpret traffic signs and suggest appropriate actions, emphasizing the importance of recognizing warning signs and responding accordingly [31][32][34]. Deep Thinking and Risk Assessment - The article emphasizes the need for deep analysis of traffic participants based on their dynamic states, distances, and potential risks, leading to a ranking of danger levels among vehicles [40][42]. - The Qwen3-VL model can assess the risk of nearby vehicles, particularly in low visibility conditions, and provide safety recommendations for driving maneuvers such as overtaking [44][46][48][50]. Traffic Flow Dynamics - The article outlines the evolution of traffic flow from smooth to congested states, highlighting the critical role of disturbances that can trigger congestion, such as sudden braking or road obstructions [60][62]. - It discusses the mechanisms of congestion propagation and the importance of maintaining safe distances and speeds to prevent accidents during high-density traffic situations [66][68].