IT COULD be a dream come true for anyone who hates parking or worries about pedestrians stepping out in front of them while at the wheel.
Cars that automatically avoid other vehicles, pedestrians and obstacles could be a step closer, thanks to a project called AutoTaxi.
The aim is to develop sensors and systems to interpret critical information about a car's local environment - for example, the path of the road ahead and the presence and movement of vehicles and other obstacles on it.
It could pave the way for fully autonomous cars in the future.
Project manager Alastair Buchanan, of TRW Conekt, explained: "Developing cars that do things which are second nature to the average driver is a hugely-complex process. The vehicle has to be able to sense and interpret the vast array of information in all driving conditions.
"AutoTaxi will help identify the best combination of sensor technologies that may take this concept a step closer to reality."
Sensor technologies are already bringing real-world benefits to new car drivers. Ultrasonic parking sensors are now common, while radar in active cruise control (ACC) sets speed and distances between vehicles on high-speed roads.
Many research projects are also using sensors to help develop the transport systems of tomorrow. Advanced Transport System's Urban Light Transport (ULTra) is an example of a project that has developed driverless vehicles which run along dedicated tracks.
AutoTaxi took sensors used in automotive applications, and applied different combinations to ULTra vehicles operating on ATS's Cardiff test track. Starting with simple assessments on a figure-of-eight track, the tests investigated more complex scenarios, to simulate real-world driving conditions such as vehicles approaching from side roads and parking.
Phase one of the project linked TRW's video lane guidance and ACC radar with lidar, which uses light to measure distance and speed, as well as ultrasonic sensors. These were fine-tuned with the addition of stereo video sensors and an LED rangefinder.
Trials in November revealed potential, particularly for the LED rangefinder system. The data will help develop algorithms to interpret the information, with results due this summer.
Partners in the AutoTaxi project include Advanced Transport Systems, Praxis High Integrity Systems Ltd, Bristol University and Warwick University.
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