MIT develops a new detection and sensing system that recognizes moving objects at the corners of vehicles

[ Instrument Network Instrument Development ] According to media reports, in order to further improve the safety of the automatic driving system, researchers at the Massachusetts Institute of Technology (MIT) developed a kind of ability to perceive subtle changes in the ground reflection to determine whether there are moving objects at the corners. New system.
In the future, autonomous vehicles can use this system to avoid potential collisions with another car or pedestrian at the corner where the line of sight is blocked. In addition to self-driving car applications, drugs or item transport robots that navigate in the hospital corridor in the future can also use the system to avoid hitting pedestrians.
In a recent paper presented at the International Conference on Intelligent Robotics and Systems (IROS), MIT researchers introduced successful experiments using autopilot cars in garages and automatic navigation wheelchairs in corridors. This new system successfully defeated the traditional Lidar (LiDAR) system for the perception of the car at the corner, because the latter can only detect objects in the "field of view". Compared to laser radar, MIT's new system has been perceived more than 0.5 seconds earlier.
Researchers say this doesn't seem like much, but for autonomous cars driving at high speeds, 0.5 seconds may mean whether the collision can be avoided.
“For robotic applications that operate in environments where there are other moving objects or people around, our system can warn the robots in advance to alert pedestrians that they are approaching, thus controlling the robot to slow down, adjust the path and prepare for collision avoidance in advance. "The ultimate goal of the paper is to provide this for fast-moving vehicles on the road," said Daniela Rus, co-author of the paper, director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and professor of electrical engineering and computer science, Andrew and Erna Viterbi. An X-ray-like 'perspective' capability."
Currently, the system has only been tested in an indoor environment. Indoors, robots move much less quickly and lighting conditions are simpler, which helps the system to detect and analyze reflections more easily.
The system uses a standard optical camera that uses a series of computer vision techniques to monitor changes in reflected light intensity to ultimately determine whether the reflection is projected by a moving or stationary object and predicts the possible path of movement of the object in question.
In a separate test, the researchers installed the "ShadowCam" system they developed in a self-driving car in the parking lot, and the vehicle headlights in the parking lot were closed to mimic the night driving environment. They compared the ShadowCam system to the laser radar. In an example scenario, the ShadowCam system detects 0.72 seconds faster than a laser for a car that is turning around a column.
In the previous test, an automatic running wheelchair equipped with the ShadowCam system determined whether a pedestrian approached by detecting the reflection of a person projected on the green area at the corner.
In the previous test, an automatic running wheelchair equipped with the ShadowCam system determined whether a pedestrian approached by detecting the reflection of a person projected on the green area at the corner.
However, so far, the experiment has many limitations: for example, the experiment was only tested under indoor lighting conditions, and the research team still needs to invest a lot of work in order to adapt the system to higher speeds and complex changes. Outdoor lighting conditions. Despite this, the system allows for a promising prospect that ultimately helps autopilots better sense pedestrians, cyclists, and other vehicles on the road.
Next, the researchers will further develop the system to work in different lighting conditions indoors and outdoors. In the future, new methods will be developed to speed up the system's reflection detection and automate the annotation of the reflection-aware target area.


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