*4.3. Path Planning Application*

The process of achieving the path planning goals can be expressed as follows: The traffic parameter set *P*(*x*, *t*) (vehicle velocity, traffic density, etc.) is predicted for each sub-path, and it is provided to the vehicles in the traffic system so as to arrange the journey according to their respective characteristics. Then, through machine learning, the stability criterion model *S*(*ρ*, *u*) of all-wheel-drive (AWD) vehicles and front-wheel-drive (FWD) vehicles is established facing different traffic parameter sets *P*(*x*, *t*). Then the optimal path is chosen according to *S*(*ρ*, *u*). In the end, the optimal path for the current vehicle, among multiple paths, is obtained by using the multi-objective optimal path planning method *J*(*ρ*, *u*) proposed in this paper.

When the expected vehicle speed is higher than the traffic flow speed, it means that the vehicle needs to change lanes frequently. At a certain vehicle flow speed and density, the maximum front-wheel angle of a successful lane change can be predicted. Furthermore, we can calculate the lateral tire force of the vehicle through the vehicle lateral dynamics model. Similar to energy and time, the vehicle's tire force saturation coefficient *δ*TFSC can be used as a basis for the vehicle's path selection. Finally, depending on the travel time, energy consumption, environment (road friction coefficient), and vehicle characteristics (vehicle handling stability), one can choose a safe, efficient, and fast road for the driver.

**Figure 14.** Path planning/selection logic diagram based on multi-objective optimization.

We consider route planning of a traffic system using traffic flow model and vehicle stability criterion as an example in Figure 15. Assume that Elbert intends to drive his vehicle from point A, seen on the left side of the figure, to point B, shown on the right side of the figure. There are three routes for Elbert to choose, namely Route 1, Route 2, and Route 3. Route 1 is closer, but the traffic flow is dense and the speed is slow. The distance of Route 2 is longer, but the traffic density is small and vehicle speed can be fast while traveling. The length and traffic flow of Route 3 are moderate, between Route 1 and Route 2, but road friction is poor. At this time, the predicted traffic flow parameters, the stability criterion model, and the multi-objective optimization route planning method can help Elbert select a safe, efficient, and fast road that matches the characteristics of his vehicle.

**Figure 15.** Example of route planning for a traffic system using traffic flow model and vehicle stability criterion.

In addition, for the problem shown in Figure 15, we consider that Elbert encounters a rainy and snowy day (Route 1's and Route 2's tire-road friction coefficient is 0.4, Route 3's friction coefficient is 0.1 due to road icing). According to the statement above, he will have different options to drive All-Wheel-Drive (AWD) and Front-Wheel-Drive (FWD) vehicles. Figure 11 shows a lane change and overtaking condition when the driver is close to the preceding vehicle, the target vehicle (HV)'s speed is large, and the front vehicle (SV)'s speed is small. Furthermore, Figure 16 shows the variation in *δ*TFSC for AWD and FWD vehicles at the same steering input in an overtaking and lane change condition from Route 1 [100]. We can observe that the FWD vehicle has been destabilized, but the AWD vehicle can keep the vehicle stable under such extreme conditions. For Route 3, where the road is icy, both AWD and FWD cannot stabilize the vehicle during a lane change and overtaking condition. Therefore, although the path 1 traffic flow speed is slow, when the driver drives the AWD vehicle, the destination can be reached faster by continuously overtaking and changing lanes. However, when driving a FWD vehicle, Path 2 would be a better choice.

**Figure 16.** TFSC comparison results between an AWD vehicle with stability control and a FWD vehicle without stability control for the same steering input.

### **5. Conclusions**

Energy management strategies of new energy vehicles (NEVs) highly depend on accurate prediction of future velocity. In this paper, we present a review study on various vehicle speed prediction methods for NEVs. In this regard, various macroscopic traffic flow models, data-based traffic flow models, and the influence of vehicle lateral dynamics are introduced.

Through a detailed review and comparison of each method, it is clear that each approach is suitable for different application scenarios. Macroscopic and data-based traffic flow models are introduced and compared in terms of their pros and cons, potentially leading to better identification of development trends for prospective designers. Questions regarding the error in the macro traffic flow model, the magnitude of the error, and how to reduce the error with delay are to be answered. Moreover, prediction error between data-based and macro traffic models as well as how to combine these to reduce error remains an open headline.

Since the core issue of vehicle energy management is accurate speed forecast and planning, an emerging field, namely, vehicle stability control methods on speed prediction are investigated. Key questions on establishing a mixed traffic flow model and safety risk assessments are investigated from a traffic flow model and influence on vehicle lateral dynamics viewpoints.

The link between vehicle stability and energy efficiency is demonstrated by the application field of speed prediction methods. Benefitting from the fast development of vehicular technologies; software developers in the field of artificial intelligence; and sensors, cameras, and radars, potential future developments for velocity prediction methods could guide and inspire prospective researchers.

Lastly, examples of driving safety, traffic efficiency, and energy management are used to demonstrate the applications of speed prediction method based on vehicle handling dynamics and driving environment in path planning.

**Funding:** This research was funded by Northeast Forestry University No. 520-60201418.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

