S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Mechanical Aerospace Engineering (기계항공공학부) Theses (Ph.D. / Sc.D._기계항공공학부)
Automated Driving System with Guaranteed Safety based on Generic Environment Representation and Model Predictive Control
- 공과대학 기계항공공학부
- Issue Date
- 서울대학교 대학원
- Automated driving vehicle ; Model predictive control ; Automated driving control algorithm ; Generic Environment representation ; Safe driving envelope decision
- 학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 이경수.
- Recently, the interest of automotive researches changes from the passive safety system to the active safety system and, by extension, automated driving system due to advances in sensing technologies. For example, active safety applications, such as vehicle stability control (VSC), adaptive cruise control (ACC), lane keeping assistance (LKA) and lane change assistance (LCA) system), automated parking assist system (APA) and blind spot intervention (BSI), already have been commercialized by major automakers Furthermore, there are various ongoing projects which are trying to achieve the zero fatality. Several research teams around the world are continuously advancing the field of autonomous driving. And some of major automakers have been researching to integrate individual active safety system for the enhancement of safety. GM is trying to develop and introduce Super Cruise system which can drive on the highway without human drivers intervention. Toyota has undertaken researches to develop Automatic Highway Driving Assist technology. The BMW managed to drive 100% automated in real traffic on the freeway from Munich to Ingolstadt, showing a robust, comfortable, and safe driving behavior, even during multiple automated LC maneuvers and the Mercedes Benz developed Intelligent Drive system and followed the route from Mannheim to Pforzheim, Germany, in fully autonomous manner
From a careful review of considerable amount of literature, automated driving technology has the potential to reduce the environmental impact of driving, reduce traffic jams, and increase the safety of motor vehicle travel. However, the current state-of-the-art in automated vehicle technology requires precise, expensive sensors such as differential global positioning systems, and highly accurate inertial navigation systems and scanning laser rangefinders. While the cost of these sensors is going down, integrating them into cars will increase the price and represent yet another barrier to adoption.
Therefore, this dissertation focused on developing a fully automated driving algorithm which is capable of automated driving in complex scenarios while a chosen sensor configuration is closer to current automotive serial production in terms of cost and technical maturity than in many autonomous vehicles presented earlier. Mainly three research issues are considered: an environment representation, a motion planning, and a vehicle control.
In the remainder of this paper, we will provide an overview of the overall architecture of the proposed automated driving control algorithm and the experimental results which shown the effectiveness of the proposed automated driving algorithm. The effectiveness of the proposed automated driving algorithm is evaluated via vehicle tests. Test results show the robust performance on an inner-city street scenario.