The use and development of new Advanced Driver Assistance Systems (ADAS) based on fusion of various sensors, aiming to achieve a higher robustness, are becoming more common. In pre-crash scenarios, the extraction of information from an incoming vehicle should be done as near as possible to the moment of impact "t 0 ", where relevant issues such as disturbances in the sensor signal and partial occlusion of the vehicle play an important role. The presented work introduces a novel fusion approach between a Lidar and a camera to extract the distance and the approach angle of an oncoming vehicle. It detects the bullet vehicle’s license plate using a deep learning neural network and fuses the Lidar information to estimate its distance and approach angle. The proposed architecture was evaluated on data from a benchmark dataset - KITTI, and with a pre-crash scenario dataset collected inside the CARISSMA Research Center indoor hall facilities. The algorithm reached an accuracy of 0.16m±0.26m for the distance and 5.48°±4.99° for the angle estimation on the benchmark dataset. On the pre-crash dataset, the experimental results yielded an accuracy of 0.022m ± 0.022m and 2.82°± 1.98° for the distance and angle, respectively. The system can extract parameters until 0.4m before a collision, and can overcome blurriness and partial occlusion conditions, being also easily reproducible in other sensor setups.