Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems.
You need just two eyes and two ears to drive. Those remarkable sensors provide all the info you need to, say, know that a fire engine is coming up fast behind you, so get out of the way. Autonomous vehicles need a whole lot more than that. They use half a dozen cameras to see everything around them, radars to know how far away it all is, and at least one lidar laser scanner to map the world. Yet even that may not be enough.