Over the past few years, the progress made in conceptualizing and designing autonomous vehicles has put the deployment in top gear. But are the autonomous driving system ready for all the unexpected scenarios that the road may present? This predicament has stumped even the leading technological superpowers, including Waymo (Google), Tesla and Uber.
While the technological advancements have given a commanding position for ideators to broaden the scope, there is most certainly an increasing urgency to address the multiple driving challenges that will be presented in the real world. Let’s dive into the major hurdles that are slowing down the advancements of self-driving cars.
Mapping the roads
Road routes are ever-changing. In fact, researchers have pointed out that within a year’s span, a mere 6-miles road had multiple alterations in terms of lane markings, features and so much more. Google has addressed the routing problem by relying upon a Lidar camera affixed on top of the vehicle to generate a 3D map by leveraging its Google Maps platform. However, to map out every single road of a country, let alone the entire globe, and keep the maps updated to the constantly-changing scenarios may take some time.
On the other hand, players like Tesla are moving away from the Google Mapping concept and implementing imaging and sensor processing to move their self-driving cars forward.
From navigating roads with no lane markings, deciphering hand-signals for route diversions and traversing through two-way traffic in case of roadblocks, self-driving cars need to ramp up their detection and response strategy.
Driving means facing innumerable, unpredictable instances on a daily basis. Every journey possesses new, unplanned challenges which the autonomous driving systems have to be equipped to tackle. Say, there may be unsure pedestrians moving across the crossway, ambulance vehicles traveling in the opposite direction, drivers using flashlight signals, and so many new variables every day.
Where humans may not find it difficult to tackle such minor on-road challenges, these situations can completely halt self-driving cars. Self-driving cars need to comprehend these key factors – differentiating between pedestrians, vehicles and animals, predicting their behavior and movements and the appropriate response for such interactions.
With the increasing rate of driverless car accidents, technology innovators and data collaborators will be put to the test to build driving systems that overcome real-world scenarios and make self-driving cars a full-blown reality.