Brief

Researchers from Fudan University and Shanghai Jiao Tong University have developed a novel approach for autonomous driving that incorporates a "spatial memory" by retrieving historical geographic information. This method uses GPS data to access street view and satellite imagery of the current location, fusing this with real-time sensor data. The system is designed to provide a spatial prior, helping vehicles understand road structures like lane lines and boundaries, especially in challenging conditions where sensors may be obscured or provide limited views. This "retrieval-augmented autonomous driving" paradigm shifts from relying solely on immediate sensor input to a combination of real-time perception and historical spatial context. AI

Researchers from Shanghai Jiao Tong University and the Qwen team have introduced CodePercept, a novel approach to enhance large language models' visual perception capabilities, particularly for STEM tasks. Their research suggests that improving visual perception, rather than just reasoning, is the key bottleneck for models tackling science and math problems. CodePercept leverages code as a precise language for visual understanding, enabling models to generate executable code that accurately represents image content, thereby overcoming the inherent ambiguity of natural language descriptions. AI