# Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
## 1 Introduction  
An autonomous driving system is a fully automated and highly centralized car operation system for the work performed by drivers [1, 2, 3]. Realizing fully automatic operation can save energy and optimize the reasonable matching of system energy consumption and speed. In recent years, with the rapid development and popularization of intelligent autonomous driving [4, 5], it is a foregome conclusion that cars can achieve a higher level of autonomous driving. In the development process of intelligent driving, the Internet of vehicles is the key premise to realize intelligent driving and autonomous driving [6, 7, 8]. The Internet of vehicles is a huge interactive network composed of vehicle location, speed, route and other information. Through global positioning systems (GNSS), onboard radars, sensors, etc., vehicles can complete the collection of their own environment and status information. Through Internet technology, all vehicles can transmit all kinds of information to the central processing unit. Through computer technology, the information of a large number of vehicles can be analyzed and processed, so as to calculate the best route for different vehicles, provide a timely report concerning the road conditions and arrange the signal cycle. Route planning, which is closely connected with the Internet of vehicles, is to plan a global road route based on the current real-time traffic flow information given the starting point and target point [9, 10, 11, 12, 13]. An optimal global route can avoid traffic jams, reduce fuel consumption and improve the safety of self-driving vehicles, so the selection of optimal constraints is important when performing route planning. With the rapid development of Telematics and 5G technologies, each driverless vehicle is more likely to have access to real-time road traffic information, which supports global path planning to obtain better routes [14, 15]. There are many kinds of studies concerning path optimization, mainly in the field of road traffic and operations research [16], mostlyapplying one or more optimization objectives for path planning, but how to plan a better route in terms of time, distance, cost, etc., in real time according to the passenger's wishes under the autonomous driving field is also an important issue in this field. Therefore, developing the best and most reasonable route for autonomous driving systems, taking into account driver requirements, driving time, cost and other factors, can facilitate people's travel while promoting autonomous driving [17; 18; 19; 20]. In this paper, a general framework for a route planning method based on multi-objective optimization, considering four factors (total travel distance, total travel time, total cost and travelers' personal preference), is devised. Complex network science is applied to route planning for modeling and route generation. We summarize the main contributions of our paper as follows:  
1. We address the problem of route planning for autonomous driving in the background of complex network science. It is shown that the property of the network structure may seriously influence the performance of the traffic and the efficiency of global route planning for autonomous driving. 2. We proposed a general framework of route planning method that provides a human-computer interaction page and inputs the optimal route into the autonomous driving framework for local planning, completing the overall framework for autonomous driving. 3. We evaluate the general framework of the route planning method on a real-world traffic network and the results show that our method can generate a better route based on four factors, proving the feasibility and applicability of our method and accelerating the promotion of autonomous driving. The rest of the paper is organized as follows. A literature review concerning route planning is introduced in Section 2.