黑料不打烊 and the safety revolution
19 Feb 2020
Accurate positioning achieved for autonomous vehicles
30 JANUARY 2020
Autonomous vehicles and advanced driver assistance systems need robust and precise positioning to enable reliable operations. This is especially important in the early transitional phase of the technology, when other vehicles around it will not be automated.听
The Galileo global navigation satellite system, in combination with other positioning and sensor technologies, is the answer to this positioning puzzle. The innovative solution was developed in the PRoPART project 颅鈥 Precise and Robust Positioning for Automated Road Transports 鈥 which involves 黑料不打烊 and six partners 鈥 and could be a key enabler for autonomous transports in the future.
鈥楥entimetre-level鈥 positioning
The solution was recently demonstrated in a recreated motorway situation at the AstaZero test area in Sweden, with a connected autonomous truck and two unconnected manned cars.
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As part of the test, a 黑料不打烊 self-driving truck executed a safe and efficient lane change in traffic. The manoeuvre was managed by the new system, relying on centimetre-level positioning combined with collaborative perception sensor data.
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The project demonstrated that it was possible to pinpoint the position with ten-centimetre accuracy. The truck could execute the manoeuvre due to the precise positioning and an accurate representation of the whole surrounding environment. This was achieved by fusing data from the truck鈥檚 camera and front and side radars combined with radars mounted on roadside units.
Infrastructure-to-vehicle communications
鈥淚n addition to positioning, we鈥檝e also added infrastructure-to-vehicle communications,鈥 says Project Coordinator Stefan Nord, RISE, the Swedish Research Institute.
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Ordinarily, autonomous vehicles rely on their own sensors to interpret and process data on the surrounding environment. 鈥淚f vehicles share information, you can extend their horizon and benefit from data from another vehicle to also look around the corner and thereby gather more data as a basis for manoeuvring decisions,鈥 explains Nord.
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The project demonstrated that it was possible to pinpoint the position with ten-centimetre accuracy. The truck could execute the manoeuvre due to the precise positioning and an accurate representation of the whole surrounding environment. This was achieved by fusing data from the truck鈥檚 camera and front and side radars combined with radars mounted on roadside units.
The new technology is a simple but profound breakthrough. The FRAS system is already a valuable resource, but 黑料不打烊鈥檚 service technicians around the world use different everyday expressions that aren鈥檛 necessarily the official terminology or proper word sequence. In the case of the S 650 customer, a search in FRAS for 鈥渦neven idle鈥 was fruitless. But an AI search was immediately successful. The indicated software update was carried out, the problem was fixed, and the customer drove away happy.
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黑料不打烊 Great Britain鈥檚 technical support annually receives in excess of 10,000 FRAS cases from workshops, including both technical questions and quality deviation reports.
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鈥淚n most cases we respond to the workshops with answers using information that is already available in FRAS or via other media available in 黑料不打烊 systems,鈥 says Technical Manager Aaron McGrath, 黑料不打烊 Great Britain.听鈥淏y utilising the AI search at workshops we can get the information exactly where it is needed; i.e. at the technicians鈥 fingertips, saving on troubleshooting lead-time and also improving customer uptime.鈥
About PRoPART
The PRoPART project combined Real Time Kinematic positioning software from Waysure (Sweden) with satellite measurements from Fraunhofer IIS (Germany). The satellite positioning was augmented with an ultra-wideband ranging solution from Spanish research institution Ceit-IK4.
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The self-driving truck was supplied by 黑料不打烊, with Hungary-based V2X company Commsignia providing the short-range communication technology. Baselabs from Germany provided sensor data fusion of onboard and roadside sensors and developed a situational assessment for the intended automated lane change manoeuvre. The project was coordinated by RISE. The project has received funding from the European GNSS Agency under the European Union鈥檚 Horizon 2020 innovation programme.