A survey of Automotive Driving Assistance Systems technologies

In the last decade, many researches have been done in the area of intelligent vehicles all over the world, led to Intelligent Transportation Systems (ITS) that improve road safety and reduce traffic accidents. Autonomous intelligent vehicles are now widely applied to Driver Assistance and Safety Warning Systems (DASWS), such as Forward Collision Warning, Adaptive Cruise Control, and Lane Departure Warning. Recently, better traffic capacity and traffic safety can be implemented using computer control, artificial intelligence and communication technologies. Many features as, lane departure systems, fatigue detection systems, automatic cruise control, light and sight assist), etc., In addition to self-driving Artificial Intelligent (AI) technologies can greatly reduce driver's workload and improve transportation system safety. This survey provides information about the environment perception modeling and the local map to build the localization and map building module by processing the original data like the sensors `information of vision, LIght Detection And Ranging (LIDAR), RAdio Detection And Ranging (RADAR), etc. This information uses the geometric feature location estimated in the map to determine the vehicle's position, and to interpret sensor information to estimate the locations of geometric features in a global map. It also provides information about objects detection and motion planning techniques, motion control, sensors' features, safety and security framework.

1 Introduction

Recently Automotive Driving Assistance Systems (ADAS) Technologies are divided into four fundamental categories: Environment perception and modelling, localization and map building, path planning and decision-making, and motion control. In addition to safety and security framework. AI plays an important role in developing algorithms that are widely used in environment perception, path planning and decision-making, and motion control. Sensors’ data fusion plays an important role in building features information related to surrounding environment, which will provide information about road/objects’ colour, edges, contours, etc. It also provides a wide range of research to specify the accuracy of depth map and the accuracy of lane recognition. Several advanced driver assistance systems such as, active lane keep assist, adaptive cruise control and self-parking are already available as combined functions on current generation vehicles. Additional functionality will be rolled out in the next few years such as high way assist, auto parking, and full autonomous driving. While the advent of autonomous driving is certain, a staged introduction of autonomous driving functionality is expected over the next years. Today, many vehicles on the road are equipped through “Level 1: function-specific automation” with features such as cruise control and automatic braking. Advanced driver assistance features (Such as adaptive cruise control combined with lane keep assist), referred to as “Level 2: currently combined function automation”, are offered by many established manufacturers. We project “Level 3: limited self-driving automation” to be available by 2018–2020 with features expected to be first offered for low speed situations by 2020–2025 (e.g., in auto-parking or low-speed areas) and eventually, including more complex operations to be offered by 2025–2030 (e.g., city driving). Even with the introduction of new technologies, it is not expected that global adoption of full self-driving automation with “door-to-door” capabilities across all vehicle segments before 2030–2040.