Driving smarter cities: How GPS is shaping the future of automated driving
We live in a world where nearly any task can be outsourced to a machine, from burger-flipping to vacuuming. Many of these tasks are automated to boost productivity, enhance safety, or free us from chores we’d rather avoid. So why not entrust the task of driving to the vehicle itself? Self-driving vehicles promise not only convenience but also improved road safety, reduced human error, and the potential to ease traffic congestion. With automated driving, we could also open up mobility to those unable to drive, from the elderly to those with disabilities, creating a more accessible, efficient, and safer transportation system for everyone.
Robotaxis, such as Waymo and Tesla's recently-launched CyberCab, showcase the promise of self-driving technology because they can navigate well-defined routes in controlled environments. However, applying this technology to personal vehicles is more difficult due to the variety of driving situations and diverse environments.
Carmakers around the world are working to meet the demand for automated driving and autonomous vehicles. The road to full autonomy involves various levels of self-driving. The SAE defines these levels from Level 0 where the driver handles all tasks without automation, up to Level 5, where the vehicle achieves full self-driving capabilities with no human intervention needed. Today, we’re at a stage when the task of driving can be handed over to the vehicle under specific conditions. This is known as Level 3 and is available in some regions including California and Nevada and limited to regulatory constraints. Already adopted by Mercedes Benz, their Level 3 Drive Pilot is restricted to certain vehicles, weather conditions and freeways. Goldman Sachs forecasts that by 2030, up to 10% of global new car sales could be equipped with technology that lets drivers take their eyes off the road and their hands off the wheel in specific situations.
But as personal vehicles evolve, one day we might reach a point when human intervention is no longer required: you can sit back and relax (or get stuff done) while your car takes you to your destination.
How does automated driving work?
Autonomous cars have a complex system of sensors that enable them to see and analyse their surroundings in real time. These sensors – including GPS, cameras, radar, LiDAR, and ultrasonic sensors – help in monitoring other vehicles, pedestrians, road signs, and lane markings to create a detailed map of the environment. This information is then processed by advanced algorithms to help the car make quick decisions and react to changes in traffic conditions, obstacles or even unpredictable behaviour from other drivers.
This system is part of Advanced Driver Assistance Systems (ADAS), a range of technologies designed to enhance vehicle safety by actively assisting drivers or, in the case of autonomous cars, taking full control of navigation. ADAS includes features like collision avoidance, lane-keeping assistance, adaptive cruise control, and emergency braking, all working together to reduce human error and improve road safety. By combining data from multiple sensors, ADAS-equipped autonomous cars can navigate safely across various road conditions and environments.
GNSS (the technology that powers the blue dot on your phone map, more commonly known as GPS, the US system) uses satellites in space to pinpoint your location on the ground. By integrating GNSS with other sensors, autonomous cars can confidently navigate challenging environments, ensuring efficient and safe travel.
The urban challenge
Automated driving is currently available only under specific conditions, such as on open roads and within designated speed limits. For example, BMW’s Personal Pilot enables drivers to briefly take their hands off the steering wheel to make calls or even stream videos during specific scenarios, like traffic jams on the motorway, and at speeds of up to 60 km/h.
Currently, automated driving operates effectively on open roads, where there is a clear view of the sky, allowing the GNSS receiver to obtain an accurate signal. Expanding ADAS technologies beyond these open roads into urban cities presents unique challenges.
You might have noticed that your car satnav struggles to point you in the right direction when you’re driving in a busy urban area or through a forest. Cities like New York, Tokyo, Seoul and London are characterised by tall buildings, tree cover, and radio frequency (RF) interference, which can often disrupt GNSS signals. This leads to inaccuracies in satellite navigation that jeopardise the performance of location services. In such settings, knowing which lane a vehicle occupies becomes not just a convenience but a necessity for ensuring safety and efficiency in the expansion of autonomous driving beyond open roads.
Lane-level accuracy: The game changer
GNSS is a crucial sensor in the ADAS system and has the unique advantage of providing absolute location. By improving the accuracy of GNSS and integrating it with other relative sensors, it’s possible to determine a vehicle’s position within centimetres.
Enhanced GNSS supports various functions in autonomous cars:
System initialisation: As the only sensor providing absolute positioning, GNSS enables precise, real-time localisation, allowing ADAS to accurately position vehicles, detect obstacles, and make driving decisions like braking, turning, and lane-changing. GNSS also helps HD maps match the vehicle's location, allowing for quicker processing.
Sensor calibration: An accurate GNSS position can be used to calibrate other sensors to maintain their own accuracy. Relative sensors, that tend to accumulate biases over time, can be regularly re-calibrated using GNSS.
Geofencing: Automated driving works only within specific sections of roads. GNSS precisely defines no-go zones, allowing L3 systems to safely transfer control back to the driver in restricted areas.
Mitigating GNSS challenges to enable autonomous driving
Expanding the accessibility of autonomous vehicles involves two things:
Navigating urban areas effectively: Did you know that GNSS signals are only as powerful as a 45-watt light bulb? Any obstruction can make the signal even harder for the receiver to pick up. Enhanced GNSS accuracy can help mitigate the effects of signal reflections from buildings and trees to provide a more accurate and reliable location.
Building resilience to cyber-attacks: Autonomous cars can be vulnerable to spoofing attacks, where hackers can trick the system into giving a false location, potentially diverting the vehicle and driver. It is essential for cars equipped with self-driving technology to detect and mitigate these threats.
FocalPoint’s Automotive positioning software S-GNSS® Auto is bridging the gap to full autonomy and has a major role to play in improving safety and efficiency on our roads. It helps improve accuracy at the chipset level. Using machine learning algorithms and advanced physics, it boosts the performance of vehicle GNSS, making it more accurate, reliable and safe.
The global impact
The desire to improve lane-level accuracy is not confined to a single city or country. The push for better lane-level accuracy is a global effort that can greatly improve safety, sustainability, and accessibility in transportation. In Japan, for example, the 11th Fundamental Traffic Safety Program aims to utilise ADAS technologies to reduce traffic fatalities.
Automated and autonomous driving technologies contribute to sustainability by optimising traffic flow, reducing fuel consumption, and lowering emissions. By enhancing vehicle efficiency and enabling coordinated driving patterns, these systems help create cleaner and more sustainable urban environments.
The transition to electric vehicles, fueled by regulations like the EU's goal for all new cars to be zero-emission by 2035, is vital for autonomous driving. As electric vehicles are integrated into transportation, their advanced technologies enhance automation, promoting more efficient and sustainable urban mobility.
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