What is Tesla's official response to sensing ghosts?

[AnnieDella]

Tesla customer service stated that the vehicle's driving visual display uses cameras and sensors to detect obstacles nearby. "Sometimes, to avoid blind spots or misunderstandings, especially when owners don't wash their cars for a long time, excessive dust covering the sensors can lead to sensing errors. This is possible, and the specific lane conditions cannot be entirely judged through the screen display." The customer service representative explained the sensing errors and noted that different vehicles may experience different sensing inaccuracies, which cannot be generalized. Types of Radar: Radar is an essential component for autonomous driving, mainly consisting of three types: LiDAR, millimeter-wave radar, and camera sensors. Tesla's Sensing System: It employs a combination of cameras and millimeter-wave radar for detection to identify and perceive objects in the real environment. The images captured by the cameras include pedestrians, vehicles, animals, or other obstacles. After collecting the data, Tesla analyzes the objects captured by the algorithm and presents them visually on the central control screen.

[VanHarrison]

From an automotive repair experience perspective, let me discuss this matter. Tesla has issued multiple official responses to so-called 'ghost detection' reports, consistently emphasizing that these are not supernatural occurrences but rather false alarms from the sensor system in specific environments. For instance, cameras might misinterpret tree shadows or animals as pedestrians in low-light or foggy conditions. Through their app or customer service, they advise users to check for software updates – similar incidents occurred in 2022 when drivers reported system warnings about objects in cemeteries, which the company explained as algorithm overreactions to irregular shadows. Tesla reminds owners to keep vehicle systems updated to minimize false alerts while encouraging users to report issues to help engineers optimize performance. As a professional, I've seen similar malfunctions caused by dust accumulation or voltage instability – routine maintenance like cleaning lenses or system reboots can often resolve these. Autonomous driving relies on AI technology, which inevitably has imperfections, but the company denies paranormal explanations and promotes rational approaches to technological improvements.

[VonManuel]

As a Tesla owner, I'd like to share a personal experience. While driving in the suburbs at night, the central control screen suddenly displayed a 'Pedestrian Detected' warning, yet there was no one around. My friend jokingly said it sensed a ghost. Later, I contacted the official customer service, and they responded that it was purely a sensor error caused by light reflection or small animals moving quickly, recommending that I update the system software to fix it. The customer service mentioned this wasn't an isolated case, with similar feedback since 2021, and they classified it as a technical bug rather than anything supernatural. Now, whenever something unusual happens, I check if the cameras are clean and ensure the software is up to date. This incident taught me not to be afraid and to focus more on actual driving safety, such as manually controlling the car at night and avoiding reliance on autonomous driving in dimly lit areas. The official stance remains a scientific explanation, encouraging the reporting of issues to help them continuously optimize the design.

[RemingtonLee]

As an automotive technology enthusiast, let's talk about Tesla's official stance. Those 'ghost detection' rumors were officially attributed to AI misjudgment: the cameras are sensitive to light changes and may mistake fluttering leaves or raindrops for objects. Tesla denies any paranormal activity, emphasizing it's a software flaw that requires OTA updates to resolve. For instance, early FSD systems had imprecise shadow processing, causing false alarms. Technically, sensor algorithms prioritize safety but are prone to errors, so users are encouraged to report cases to train the AI. Simple countermeasures include cleaning lenses or choosing clear environments. From an R&D perspective, such false alarms expose autonomous driving's shortcomings, and the company is actively refining code to reduce user panic.