Radar-Based Lane Estimation with Deep Neural Network for Lane-Keeping System of Autonomous Highway Driving

In this paper, we propose a novel radar-based lane estimation method using Deep Neural Network(DNN) without vision sensors. First, the feature vector is selected using data coming from radar and in-vehicle sensors. The feature vectors are stacked and entered into the network so that the input of the network has spatiotemporal information of the relative motion between the ego vehicle and a leading vehicle. We used a parallel structure of the DNN to estimate the road lane model for the Lane-Keeping System(LKS). The Scaled Conjugate Gradient method is adopted for optimizing the neural network. We performed a comparative study between a vision sensor and the proposed system. From the experiment results, the proposed scheme outperforms the vision system when the vision system becomes failure due to environmental effects such as shadows or lane contamination. It is expected that the proposed method is sufficient to improve the performance of LKS if the proposed system is fused with the vision system for fail-operational lane-keeping system of autonomous highway driving.