There are now road car models on sale today, from Top 10 OEMs who were early adopters of rFpro technology, whose testing began in rFpro in order to reduce costs and time delays in the engineering process.
rFpro provides driving simulation software, and 3D content, for Deep Learning Autonomous Driving, ADAS and Vehicle Dynamics testing and validation.
rFpro is being used to train, test and validate Deep Learning systems for ADAS and Autonomous applications. rFpro’s weather and physically modeled atmosphere, delivering real-time reflections, shadows and lighting mean rFpro can save years from your ADAS, sensor and Autonomous development projects.
Hundreds of kilometers of digital models of public roads are available off-the-shelf, from rFpro, spanning North America, Asia and Europe, including multi-lane highways, urban, rural, mountain routes, all copied faithfully from the real world.
The unique feature of rFpro, compared to traditional driving simulators, is that it allows driving simulation to be used to test the vehicle dynamics of road vehicles. By delivering a high resolution road surface in real time, while generating accurate realistic graphics without lag, professional test drivers may contribute to the engineering process while the car design is still model based.
This means that an rFpro driving simulator, or engineer’s Workstation can allow you to conduct testing and calibration with your professional test drivers and engineers without the delays and cost associated with waiting for the testing of real cars or prototypes.
The saving in risk and cost can be significant and we have seen full scale systems pay for themselves on the first vehicle virtual test project.
By testing earlier in the engineering process on an rFpro driving simulator, the changes identified through test driving can be made while the cost of implementing those changes is still relatively low; before prototypes are required and before production. The cost savings can be substantial.
rFpro is being used to test and calibrate passive chassis designs, steering systems, chassis control systems, ADAS and Autonomous sensor models, algorithms and control systems, drivetrain control systems, traction control, stability control, torque vectoring and engine control systems. As well as to train, test and validate deep learning autonomous driving models.