The information provided by the vehicle’s sensors allows the estimation of critical parameters in a pre-crash scenario. However, the available open multimodal datasets focused on optical sensors usually present a low-resolution radar. Moreover, the sensors’ positioning does not provide a representative view of the objects in the last meters around them. With this in mind, we introduce a new approach to fuse camera, lidar, and radar of high resolution at a very early stage using a fully convolutional network and an anchor-free strategy to detect the front side of a vehicle, estimating distance and orientation in a single step. A robust experimental protocol based on a new multimodal dataset shows that the proposed fusion of the three sensors brings more stability and accuracy in detection and regression tasks by better representing far and near ranges around the vehicle where the sensors response to objects change significantly.