Addressing the challenges of autonomous driving

November 05, 2018 // By Willard Tu
Addressing the challenges of autonomous driving
One of the hottest topics within the automotive world at the moment is autonomous driving, and both existing automotive manufacturers and new entrants to the automotive market are developing vehicles which have autonomous capability. These developers of autonomous capability are benefiting from Moore’s law, which has enabled significant increases in processing capability and sensor technology while also lowering the cost. Willard Tu

The term autonomous driving is a complex area which spans a range of capabilities from fully autonomous to shared control with the driver. To help classify the different autonomous capabilities the Society of Automotive Engineers has defined several levels which outline the autonomy:

SAE autonomous driving level definition.

As the level of autonomy increases, so too does the need for the vehicle to comprehend its environment and safely act within this same environment. Enabling the vehicle to understand and safely interact with its environment requires a number of sensor modalities from ultrasonic, GPS, RADAR, and cameras to LIDAR along with associated processing capabilities.

Each of these different modalities provides data on the vehicle’s overall environment; forming a complete picture requires the autonomous vehicle fusing these elements together. The different sensor deployments and modalities will vary depending upon the level of autonomy being implemented. However, cameras will be used for applications such as lane keeping assistance, blind spot detection and traffic sign recognition, while RADAR implemented as Frequency Modulated Continuous Wave (FMCW) may be used to determine the distances to objects. For levels two and above, the ability to comprehensively understand the vehicle’s environment is crucial. This enables the vehicle to identify its location and surrounding obstacles allowing safe navigation. Understanding of its environment is obtained using camera, RADAR and LIDAR along with global positioning system (GPS) data. GPS data on its own cannot be relied upon as its accuracy varies, while also being easily blocked by buildings and infrastructure. 

Understanding their environment and taking actions are key enablers of the autonomous capabilities.  Both lives and the environment are at risk as the result of an unintended operation or action at all six levels. As such, autonomous capability must be developed within a framework which ensures safety of the design and all its elements. Developing according to ISO26262 is an absolute must for autonomous vehicles. This standard provides a framework which, if followed, ensures safety, defining several Automotive Safety Integrity Levels (ASIL) along with their allowable failure rates. Autonomous driving solutions will also be subject to a range of harsh environments once deployed in markets around the world. To ensure systems operate across these environments, automotive grade components require extensive qualification and certification to and beyond AEC-Q100.

Design category: 

Vous êtes certain ?

Si vous désactivez les cookies, vous ne pouvez plus naviguer sur le site.

Vous allez être rediriger vers Google.