An entire industry is charging ahead to reach higher-level autonomous driving capabilities. Still, the challenges are many, ranging from the purely technical to regulation- and insurance-related topics, all the way to moral considerations of derived actions and decisions.
However, the benefits of Level 4 and/or Level 5 autonomous-driving capabilities, as defined by the Society of Automotive Engineers, are also many, particularly with regard to fewer accidents and life-long mobility. This means every aspect of the driving experience will change, with designers at the forefront as they now look to incorporate artificial-intelligence (AI) capabilities to help achieve the highest levels of automation as safely as possible.
The technical challenges to autonomous vehicles, like those facing high-performance wireless networks and low-latency cloud infrastructure, are solvable over time by advancing the state-of-the-art in well-understood design practices and techniques. However, based on the foreseen complexity of an autonomous vehicle, AI systems are more than a promising element to address a huge set of data, scenarios, and real-world decisions a human brain—consciously or subconsciously—today processes within a short period. And to make all of those decisions with high precision while operating a vehicle.
The focus now is to properly identify, manage, and control the actual input parameters coming from various sensors that are required to develop a usable representation of the real-world operating environment and status of the vehicle.
These sensors include cameras, radar, LiDAR, ultrasound, and other sources, such as accelerometers and gyroscopes. Many are already widely used in advanced driver assistance systems (ADAS). However, a key challenge here is to define and develop models to find correlations between available physical signals, existing or to-be-developed AI scenarios, deep-learning models, and the real-world decision impact in a real traffic situation.