## 5 Transport
### Predicting the Intent to Return to a Vehicle  
In this paper, we present a use case of driver assistance. Here, the vehicle can determine if and when the driver is returning, ideally aiming to find it out as early as possible  
Figure 14: The classification of autonomous vehicles starting from the no automation to the highest automation category [84; 85]. Figure 13: An overview of the intelligent transport systems, showing the main research areas, that is, smart infrastructure, intelligent vehicles, and intelligent public transport systems. and before the start of a journey. Knowing the intent of the driver can help the vehicle to do the following:  
1. Deliver a safer, personalised, and more pleasant driving experience by the timely adaptation of the car interior to prior learnt preferences or the driver profile (e.g., adjusting seats and pre-configuring the infotainment system, and adapting the human-machine interface (HMI), for example, warming/cooling the vehicle. );
2. Improve the security features by efficient activation of the key-fob scanner (e.g., for keyless entry or engine start) and exterior-facing vehicle sensors (e.g., cameras for driver recognition). In this use case, we address the problem of establishing the driver's intent to return to the car and estimating the time of arrival from his/her available partial location trajectory, possibly in a connected vehicle environment (illustrated in Figure 15). This track can be provided by a dedicated user-to-vehicle positioning solution or the user's smartphone Global Navigation Satellite System (GNSS) service. The problem is tackled within a Bayesian object tracking framework [91]. Unlike typical data-driven methods [93], this case proposes a simple prediction solution with notably low training requirements. It utilises the incorporation of contextual information, such as the user's (learnt) patterns of behaviour, calendar events, location, time of day, etc. To ensure that the framework is flexible and generic, it encloses the variations in the user motion on his/her way to the vehicle by assuming a stochastic motion model. Using the continuous-time observation model with a random noise component, the framework can also treat irregularly spaced and imprecise user location measurements. In this use case, a Bayesian Framework is used to calculate the probability that the driver is returning to the vehicle. In particular, it calculates the probability of two hypotheses: (\(H_{0,n}\)), which refers to user \(n\) returning to the vehicle and (\(H_{1,n}\)) which refers to the user not returning to the vehicle. The probability of (\(H_{0,n}\)) is determined using contextual information, such as time of the day and calendar. It also includes the user's motion model, using a stochastic differential equation. An android smartphone (assisted) GPS service collects the measurement of the instantaneous location at a rate of one per second (1 Hz). The framework calculates the probabilities of the driver returning to the vehicle. The sequentially estimated probabilities are shown in Figure 16. Figure 16a shows the results when the user returns to the car, and Figure 16b exhibits the inference outcome when the user walks towards and then past the car. For the real-time system response for these two trajectories, please refer to the attached video (available at: [[https://youtu.be/0](https://youtu.be/0) wHG-HqByyI]([https://youtu.be/0](https://youtu.be/0) wHG-HqByyI), accessed on 4 May 2021). By looking at the \"Probability of Returning to the Car\" graphs, you can see that the proposed framework provides early, successful predictions in both scenarios. In both scenarios, the probability of returning to the car is estimated by the framework to be significantly higher after 35 s, which can be considered to be e  
Figure 15: Smartphone-based implementation of the proposed solution. The instantaneous location of the smartphone is determined using the Global Navigation Satellite System (GNSS) service, and shared with the vehicle via a cloud service.