How AI will help pave the way to autonomous driving

May 09, 2018 // By Michael Reser
Artificial intelligence will enable vehicles to manage, make sense of, and respond quickly to real-world data inputs from hundreds of different sensors, but it’s going to take some time.

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.


Sensors are already used to map the terrain for
ADAS-equipped vehicles, but AI will build on these
blocks for better safety, more convenience, and energy
efficiency. (Source: Keysight Technologies Inc.) 

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.


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