At the turn of the century , the idea of a “ horseless carriage ” was met with skepticism by those forced to share the road . Today those reservations are rekindled with the prospect of the driverless car . And though removing the human element seems as inevitable as embracing the engine , autonomy on the roadways comes with its own set of concerns , with safety seated at the top of the list .
YET BEFORE ANY CAR can control itself , it has to be smart enough to do so — that ’ s where UC San Diego researchers are steering the conversation . In the lab of electrical engineering professor Nuno Vasconcelos , researchers are working on technologies that will help cars autonomously identify objects and react quickly enough to avoid collisions . The technology is part of broader interdisciplinary efforts at UC San Diego aimed at creating robotic and software systems that can better cooperate with humans . This requires developing systems capable of interpreting and responding to humans , other robotics and the environment in real time . “ We ’ re aiming to build vision systems that will help computers better understand the world around them ,” says Vasconcelos , who is affiliated with the Contextual Robotics Institute and the Center for Visual Computing , both at UC San Diego .
The lab ’ s latest breakthrough shows promise behind the wheel : a new pedestrian detection system that performs with higher speed and accuracy than ever before . Instead of costly sensor technology , Vasconcelos ’ system uses a dashboard camera and software that can process video images closer to real-time ( two to four frames per second ) with nearly half the error of image recognition systems developed by other academic and corporate research teams .
“ It ’ s much cheaper to use cameras instead of sensors to identify pedestrians ,” says Vasconcelos . “ Some sensors cost more than the car itself . We ’ ve shown that the images do not need to have high resolution in order for the system to work well .”
Naturally , the secret is in the software . Vasconcelos and his team designed the new pedestrian detection system to “ think ” via a novel algorithm that combines a traditional computer vision classification architecture , known as cascade detection , with the more complex technology of deep learning models . While each system alone has its pros and cons , the two complement each other and the balance between them allows for maximum accuracy as well as efficiency .
Cascade detection is a well-known process that works over multiple stages to crop out areas in an image that do not
The Team Behind the Machine Mohan Trivedi ( pictured fourth from right ), a professor in the Department of Electrical and Computer Engineering , stands with his team and one of the vehicles they use for their research .
contain the desired object — in this case , pedestrians . In early stages , the algorithm quickly identifies and discards areas that it can easily recognize as “ lacking a person ,” like the sky or an empty road . In the later stages , the algorithm processes areas that are considerably harder to classify , such as a tree , which could be recognized as having person-like features due to its shape , color and contours . While this method is fast initially , it isn ’ t quite powerful enough to distinguish between a pedestrian and very similar objects during the final stages .
This is where deep learning models come in . Deep learning models are capable of complex pattern recognition , which they perform after being trained with hundreds or thousands of examples . But because of their complexity , deep learning models process too slowly for real-time implementation .
40 TRITON | SPRING 2016