Abstract This paper presents a model to predict the market penetration of autonomous cars for passenger transportation, focussing on autonomous taxis without a steering wheel. For this, a discrete system dynamics model was created and evaluated where the input parameters have been taken from the literature and a survey. In this survey, user demands were investigated among 873 participants. It showed that gender, age, job situation, city size and monthly income have an impact on the trust in the technology of autonomous driving as well as on the willingness to use autonomous taxis. In contrast, no evidence of an impact was found for the respondent’s education or the numbers of adults or children in the household. The results indicate that the majority of autonomous driving vehicles will be private and will have a steering wheel within the scope of this study, which covers the time frame until 2040. These are expected to reach 12.4 million units. At the same time, autonomous taxis will enter the market. These are expected to be mainly non-controllable (without a steering wheel), reaching a maximum of 2.4 million units by 2038. This maximum mainly depends on the trust in the technology as well as on people’s willingness to give up their own car due to cost or usage factors. As an autonomous taxi supports more people on average, the total number of vehicles is expected to drop from 45.1 million to around 41.7 million by 2040. Furthermore, the percentage of people travelling primarily by taxis and public transport is expected to increase from today’s 20.0% to about 32% in 2040. Then, about 19% will use autonomous taxis at least occasionally. Lastly, the vehicle miles travelled are expected to increase by 25% with people switching from public transport to autonomous taxis.

Market Development of Autonomous Driving in Germany

Model predictive path tracking control for automated road vehicles: A review

Research on Ships Trajectory Tracking Algorithm Based on Adaptive Control

Sloped terrain is common in practice. This paper proposes a lateral controller for controlling the autonomous ground vehicle (AGV) to track the reference path on sloped terrain. The impact of sloped terrain on AGV’s motion is analyzed. The lateral disturbance from gravity is modeled, and a feedforward compensator is designed to counteract the effect of the lateral disturbance. A lateral controller consisting of a feedforward compensator and a feedback controller based on linear time-varying model predictive control (LTV-MPC) is proposed. Simulations are carried out to evaluate the control performance. The controller proposed in this paper is compared with an LTV-MPC-based feedback controller which models the lateral disturbance from gravity. It is shown that the controller proposed in this paper can achieve better control performance. 

Lateral Controller with Feedforward Compensator for Autonomous Ground Vehicle Tracking Path on Sloped Terrain

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems.

An Overview about Emerging Technologies of Autonomous Driving

. Air pollution, energy consumption, and human safety
issues have aroused people's concern around the world. This phenomenon could
be significantly alleviated with the development of automatic driving
techniques, artificial intelligence, and computer science. Autonomous
vehicles can be generally modularized as environment perception, path
planning, and trajectory tracking. Trajectory tracking is a fundamental part
of autonomous vehicles which controls the autonomous vehicles effectively
and stably to track the reference trajectory that is predetermined by the
path planning module. In this paper, a review of the state-of-the-art trajectory
tracking of autonomous vehicles is presented. Both the trajectory tracking
methods and the most commonly used trajectory tracking controllers of
autonomous vehicles, besides state-of-art research studies of these controllers,
are described.

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Lei Li

Papers

Review article: State-of-the-art trajectory tracking of autonomous vehicles