Lateral and Longitudinal Motion Control of Autonomous Vehicles using Deep Learning

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

“…Agent rt wt+1 at steering wheel. Various models as described in (Smolyakov,Frolov [39], Woo, Yu [40], Wang, Wen [41], Sharma, Tewolde [42] & Simmons, Adwani [43]) predict continuous steering commands from raw input pixels applying various approaches; some achieve this through an end-to-end method. Even though these models offer a high level of accuracy, what happens on the various layers of the network remains unknown, which makes it a prerequisite for the car manufacturing companies and their first-tier suppliers to apprehend and lawfully verify that these approaches yield the correct output before they can be adopted for commercialized AVs [44].…”

“…However, it had slow camera latency performance, that is about 300-350 milliseconds. [42] proposed CNN to implement the longitudinal and lateral control of vehicles through training two separate models to predict the speed and steering angle. CNN used 5*5 kernel and 2*2 max-pooling for each convolution.…”

“…Driving simulators are possibly the best state-of-the-art software of computer-aided kinematic and dynamic simulation and can be regarded as one of the biggest triumphs in the field, as considered by Jia [86]. Although various driving simulators have been developed, in this paper we consider only the driving simulators used in the project we have reviewed; for example, the Car Simulator (CARSIM) [2], the Open Racing Car Simulator (TORCS) [19], [42] Prescan [79], CarND Udacity [39], Gazebo [18], Udacity [83], and Grand Theft Auto V (GTAV) [41]. However, other researchers did not provide information on the driving simulators used in the project.…”

“…[77] Torch 7 Scientific computing framework for extensive support within the machine learning framework [2] CARSIM Provides labeled data for the training [19] TORCS 1.3.7 Used as research platform; runs on linux; used for car racing; allows users to develop their own vehicle controllers without human intervention [42] TORCS Used as research platform; runs on linux; used for car racing; human driver controls the vehicle in the simulation and enables the collection of data [79] PreScan Physics-based simulation platform utilized in the automotive industry to develop advanced driver assistance systems based on sensor technologies such as laser/LiDAR, GPS and camera [39] CarND Udacity Flexible software; allows simulation of car movement in manual and automatic mode [18] Gazebo Built using Robot operating system in Ubuntu; safety control module; evaluated performance of tracks in software before real track test [83] Udacity Allows flexible simulation environment [41] Grand Theft Auto V. (GTAV) Action adventure gaming platform for driving [81] Grand Theft Auto (GTA) Video game platform for driving [41] Carla Open source urban driving simulator; provides rich scene and numerous sensor information comprising camera, depth, LIDAR measurements, etc. [77] Operated in diverse road conditions like highways, local and residential roads in rain, sun, fog, day and night with lighting and cloudy conditions [2] Road course, traffic, wind and screen captures [19] Lane markings in tracks mimic similar to real world; not all traffic involved; other cars controlled by the games AI engine created nuisance and instigated crashes [20] Varied widespread driving conditions [42] 40-meter long course with right and left-hand curves and traffic lights [79] Valet parking scenario [39] Modeled on one-hour driving in different styles in manual mode; tried not to collide with objects and not to move out of track; allows modeling styles that can be implemented on special polygons in real world only [18] Salient features: boundaries of tracks and parts of buildings [40] Based on path following [76] Considers straight paths and daytime driving only. [48] Videos captures highway driving in clear weather and daytime; various road types like residential with and without lane markings; .also contains all activities of the driver like staying in and switching lanes.…”

“…Adding autonomous longitudinal control remains an unresolved problem [19]. Therefore, researchers should improve the system by gathering more data from various tracks containing various driving scenarios (road conditions) to achieve successful control [42].…”