Environment vision and understanding is a crucial task in Autonomous Driving (AD) context. This mainly needs image processing approaches such as Convolutional Neural Networks (CNN). Nevertheless, cameras have shown their limits for such a task, especially in dealing with difficult light conditions. LIDAR is a powerful and widely used sensor for AD. Indeed, LIDAR can then cope with the lack of information gathered from cameras. For AD, data processing from the sensors is the key function to obtain a high quality perception. For this, Graphics Processing Unit (GPU) platforms show great performances and outperform other processing platforms such as FPGA and Multi-cores. This work presents a new approach to produce multiple 2D representation from 3D points cloud coming from LIDAR. The 2D representation can therefore be used by any efficient image processing applications. Our approach uses only LIDAR sensor and exploits the high GPU parallelism for its implementation. The resulting 2D representations are then used by CNN for AD applications such as image classification and segmentation. Finally, our contributions have been evaluated using the KITTI road benchmark and showed encouraging results.