 The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
<clarity>  The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
 The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control subsystems. The approach generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.