Many high-end cars today come equipped with brake assist systems, which help a driver use the brakes correctly depending on particular conditions in an emergency. But what if the car could apply the brakes before the driver even moved?
This is what German researchers have successfully simulated, as reported in the Journal of Neural Engineering. With electrodes attached to the scalps and right legs of drivers in a driving simulator, they used both electroencephalography (EEG) and electromyography (EMG) respectively to detect the intent to brake. These electrical signals were seen 130 milliseconds before drivers actually hit the brakes—enough time to reduce the braking distance by nearly four meters.
Seated facing three monitors in a driving simulator, each subject was told to drive about 18 meters behind a computer-driven virtual car traveling at about 100 kilometers per hour (about 60 mph). The simulation also included oncoming traffic and winding roads. When the car ahead suddenly flashed brake lights, the human drivers also braked. With the resulting EEG and EMG data, the researchers were able to identify signals that occurred consistently during emergency brake response situations.
坐 在有三块显示器的模拟器里，每个受试者被要求行驶大约18米，跟在一辆由电脑驾驶的时速100公里（60英里）的虚拟车辆后。模拟同时也包括正面驶来的车 辆和行驶在蜿蜒的路上。当前车突然亮起刹车灯时，人类驾驶员也踩下了刹车。根据脑电图和肌电图给出的数据，研究人员能够鉴别在紧急制动反应测试中发出的电 信号。
“None of these [signals] are specific to braking,” says Stefan Haufe, a researcher in the Machine Learning Group at the Technical University of Berlin and lead author of the study. “However, we show that the co-occurrence of these brain potentials is specific to sudden emergency situations, such as pre-crash situations.” So while false positives from the signal are possible, the combination of EEG and EMG data makes a false positive much less likely.
“这些信号 并不与刹车直接相关”柏林科技大学机械学习小组研究人员兼报告作者Stefan Haufe说道，“但是，我们能证明这些大脑潜意识与突然的紧急情况有关，比如要撞车前的时候。” 因此尽管可能出现假阳性信号，但是脑电图和肌电图的组合可以将假阳性的几率大幅降低。
While this kind of brain and muscle measurement works in lab conditions, the next step—real-world application—will likely be much more difficult technically to arrange. The first thing Haufe and his team will investigate is whether or not it’s possible to accurately gather data from EEG and EMG measurements in a real-world condition. In the lab, participants were asked not to move while attached to the wires, but real-world drivers move around however they please.
虽然这种 基于大脑和肌肉的衡量手段仍只能在实验室中发生，下一步——实地测试——将会有远比实验室中更困难的技术难题。Haufe和他的团队首先要考虑的一个问题 就是在真实世界中是否可能准确地收集到脑电图和肌电图数据。在实验室中，受试者被要求在连着电线时不要移动身体，但在真实情况下他们会随心所欲地动来动 去。
“The current challenge is to determine how to make use of the important, but still small and unreliable, information that we can gather from the brain on the intent to brake,” says Gerwin Schalk, a brain-computer interface researcher at the New York Department of Health’s Wadsworth Center.
Although research into mind-reading-assisted braking systems will continue, tests involving real vehicles are likely many years away. The research may never lead to a fully automated braking system, but it could ultimately result in a system that takes brain data into account when implementing other assisted-braking measures.
Whether drivers would feel comfortable handing over any braking responsibility to a computer hooked up to their head is another question. “In a potential commercial application, it of course would have to be assessed whether customers really want that,” adds Haufe.