# Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
## 3 Proposed Method  
Given the limitation of only one or a combination of several factors, we propose to generate the optimal route considering four factors: travel time, travel distance, travel cost and personal preference. Since the personal preference would be quite different at each time for the same traveler in reality, we design a general framework for the route planning method, where a personal preference would be set for each trip. Generally, our framework for the route planning method could be applied to autonomous driving, which has an interactive interface that needs personal preference input; at the same time, the traffic information in the vehicle Internet needs to be transmitted to the ground control center. After inputting the starting point, ending point and personal preferences, the framework outputs the optimal route and transmits the route to the automatic driving framework for trajectory planning in real time.  
To solve the route planning problem for autonomous driving, a complete direct graph \(G=(V,L)\) is used, where \(V\) is the set of all nodes in the network and \(L\) represents the set of all links in the network. In this paper, the nodes and links in the transportation network have many attributes. Let \(V_{i}^{c}\) be the capacity of node \(i\), and \(L_{ij}^{c}\) be the capacity of the link between node \(i\) and \(j\). \(D_{ij}\) stands for the travel demand between nodes \(i\) and \(j\), which would be the major factor influencing the planned route. Each link \(L_{ij}\) has another two characteristics: the distance (\(d_{ij}\)) between nodes \(i\) and \(j\) and the travel time (\(t_{ij}\)) between nodes \(i\) and \(j\).  
We assume that the driverless vehicles mentioned in this paper are all fuel vehicles, which will cause increased fuel consumption when encountering traffic jams. At the same time, each vehicle has load capacity and mileage capacity and the mileage capacity of each vehicle can complete the journey it needs. At the beginning of route planning, each vehicle is located at a node in the transportation network. The time of node passing through the intersection is positively correlated with the number of intersections at the intersection. There can be no duplicate sections in the planned route and each node can only be accessed once at most. Every car can obtain instant information about road conditions at any time through the Internet of vehicles.  
Based on the above problem definitions and assumptions, the goal of autonomous driving route planning can be set to minimize the comprehensive cost synthesized in four aspects (travel time, travel distance, travel cost and personal preference). The specific objective equation is as follows:  
\[\begin{split} min&\alpha Nor(TT_{ij})\times x_{ij}+ \beta Nor(TD_{ij})\times y_{ij}+\gamma Nor(TC_{ij})\times z_{ij}\end{split} \tag{4}\]  
where \(Nor(TT_{ij})\), \(Nor(TD_{ij})\), \(Nor(TC_{ij})\) are the normalization of travel time, travel distance, travel cost respectively; \(\alpha\), \(\beta\), \(\gamma\) are the manually input variables of personal preferences on the normalization values; \(x_{ij}\), \(y_{ij}\), \(z_{ij}\) represent the limited parameters of personal preference concerning time, distance and cost. These parameters (containing \(\alpha\), \(\beta\), \(\gamma\), \(x_{ij}\), \(y_{ij}\), \(z_{ij}\)) need to be input and determined in advance before the route planning process. The purpose of normalization is to compress the data within the range of [0, 1], whose calculation formula is:  
\[Nor(X)=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{5}\]  
The settings and constraints of the general framework of route planning would be introduced separately as travel time, travel distance, travel cost and personal preference as follows.