Intersection Vehicle Turning Control for Fully Autonomous Driving Scenarios

Abstract

Currently the research and development of autonomous driving vehicles (ADVs) mainly consider the situation whereby manual driving vehicles and ADVs run simultaneously on lanes. In order to acquire the information of the vehicle itself and the environment necessary for decision-making and controlling, the ADVs that are under development now are normally equipped with a lot of sensing units, for example, high precision global positioning systems, various types of radar, and video processing systems. Obviously, the current advanced driver assistance systems (ADAS) or ADVs still have some problems concerning high reliability of driving safety, as well as the vehicle’s cost and price. It is certain, however, that in the future there will be some roads, areas or cities where all the vehicles are ADVs, i.e., without any human driving vehicles in traffic. For such scenarios, the methods of environment sensing, traffic instruction indicating, and vehicle controlling should be different from that of the situation mentioned above if the reliability of driving safety and the production cost expectation is to be improved significantly. With the anticipation that a more sophisticated vehicle ad hoc network (VANET) should be an essential transportation infrastructure for future ADV scenarios, the problem of vehicle turning control based on vehicle to everything (V2X) communication at road intersections is studied. The turning control at intersections mainly deals with three basic issues, i.e., target lane selection, trajectory planning and calculation, and vehicle controlling and tracking. In this paper, control strategy, model and algorithms are proposed for the three basic problems. A model predictive control (MPC) paradigm is used as the vehicle upper layer controller. Simulation is conducted on the CarSim-Simulink platform with typical intersection scenes.

Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function

“…MPC algorithms depend heavily on precise measurements of process variables that are provided by sensors. In some versions of automatic control systems, e.g., those described in [ 12 , 14 , 15 ], it is stressed that all necessary variables must be measured because otherwise, a significant loss in control performance is unavoidable. If the measurements are not available, the typical approach is to perform on-line estimation using Kalman or Extended Kalman filters [ 16 ].…”

A matched case-control analysis of autonomous vs human-driven vehicle accidents

“…Situational awareness of AVs can be defined as the ability of these vehicles to perceive essential elements in their surroundings, understand the importance of these elements, and anticipate their future state or changes 50 . The complexity of turning in autonomous driving scenarios arises from three primary challenges: choosing the appropriate lane (target lane selection), devising and computing a safe and efficient path (trajectory planning and calculation), and executing the turn while adjusting to dynamic conditions (vehicle controlling and tracking) 51 . AVs rely on sensors and algorithms to perceive their surroundings and make driving decisions 45 .…”

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study

“…Second, the efficiency of control algorithms relies on the accuracy of sensors, e.g., in UAV [ 31 ] or autonomous vehicle [ 32 , 33 ] control. It is necessary to point out that the efficient operation of control algorithms can be significantly improved using neural networks.…”