Dr. Karl Kummer Institut: Autonomous Vehicles are getting ready for the fast lane!
Updated: Apr 29, 2022
Connected vehicles have been widely touted as the future of safe and highly optimised transport. Many car makers are eager to develop vehicles with connected features including tracking cameras, radar sensors, laser scanners, ultrasonic sensors, LIDAR and GPS; all aimed at creating an intelligent vehicle that can communicate, not only with other vehicles (V2V), but infrastructure and land- scape around it (V2I) (Bagloee et al., 2016). These connected vehicle systems provide automated vehicles with information that may not be available through internal sensor equipment alone. For example, V2V applications would warn vehicles about a vehicle suddenly braking in front of them. Similarly, roadway-based sensors in a V2I application might detect the presence of a pedestrian, who is about to cross the road in the vehicle’s sensory blind spot (Baker et al., 2016). Furthermore, AVs (autonomous vehicles) rely on AI software based on deep learning techniques. This approach works by teaching the vehicle how to drive while maintaining safe headways, lane discipline and control during testing (Abduljabbar et al., 2019). Enablers, barriers and opportunities – how far are we from full market penetration? AVs operate on a three-phase design known as “sense-plan-act” which is the premise of many robotic systems. The main challenge for AVs is understanding and reacting appropriately to the complex and dynamic driving environment. To this end, the AVs are equipped with a variety of sensors, cameras, radar and so on which obtain raw data from the surrounding environment (Internet of Things or IoT).
These data then serve as the input for software which would recommend the appropriate course of action (AI) such as acceleration, lane changing or overtaking (Bagloee et al., 2016). The levels of automation can vary from partial to full automation.
Some of the key technologies used in AVs are: - video cameras (tracking of lane markings and reading road signs) - radar sensors (detecting objects ahead) side laser scanners - ultrasonic sensors
- differential GPS - mapping
- infrared cameras
- LIDAR (detection of objects in 3D)
Some of the potential advantages and opportunities of AVs include:
- optimisation of traffic flows (reducing congestion, saving fuel, improving air quality)
- making transport safer and more efficient
- driver convenience and quality of life
- more travelling opportunities for those unable to drive
- reduces land required for parking
Some of the disadvantages and potential barriers for implementation include: - large investment in infrastructure required
- high cost of autonomous driving systems
- liability issues, for example insurance claims
- regulatory changes required at all levels (national, regional, local)
- public perception
- giving up control,
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