# Road Vehicles Surroundings Supervision:
## 2 The Present Issue  
This Special Issue consists of 11 papers covering some of the most relevant topics related to road vehicle surroundings supervision, providing an overview of technologies and algorithms that are currently under research and deployment. These papers could be divided in two main groups. The first deals with specific technologies or solutions [1; 2; 3; 4; 5; 6; 7] and the second presents applications or architectures for complete autonomous vehicles (or parts of them) [8; 9; 10; 11]. Furthermore, the first group presents a quite wide vision of research fields that could be involved such as onboard sensors [1; 2; 3], communications [4], driver supervision [5], and traffic analysis [6; 7]. Onboard perception systems provide knowledge of the surroundings of the vehicle, and some algorithms have been proposed to detect road boundaries and lane lines. This information could be used to locate the vehicle in the lane. However, most proposed algorithms are quite partial and do not take advantage of a complete knowledge of the road section. An integrated approach to the two tasks is proposed in [1] that provides a higher level of robustness of results for road boundary detection and lane line detection. Furthermore, the algorithm is not restricted to certain scenarios such as the detection of curbs; it could be also used in off-road tracks. The next paper [2] also considers that global navigation satellite systems (GNSS), to achieve the necessary performance, must be combined with other technologies into a common onboard sensor set that allows the cost to be kept low and which features the GNSS unit, odometry, and inertial sensors. However, odometers do not behave properly when friction conditions make the tires slide. The authors introduce a hybridization approach that takes into consideration the sliding situations by means of a multiple model particle filter (MMPF). Tests with real datasets show the goodness of the proposal. Considering that monitoring systems for intelligent vehicles that employ remote sensors, such as cameras and radar, require the tracking of moving objects, adaptive tracking techniques are commonly used for this purpose. To this end, a gain design strategy to compose an optimal \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter is proposed [3]. Vehicular communications, both between separate vehicles and between vehicles and infrastructure can be seen as an extension of the knowledge a vehicle can obtain from the surroundings. VANETs (Vehicular Ad-hoc Networks) are an emerging offshoot of MANETs (Mobile Ad-hoc Networks) with highly mobile nodes. They are envisioned to play a vital role in providing safety communications and commercial applications to the on-road public. Establishing an optimal route for vehicles to send packets to their respective destinations in VANETs is challenging because of the quick speed of vehicles, dynamic nature of the network, and intermittent connectivity among nodes. A novel position-based routing technique called Dynamic Multiple Junction Selection based Routing (DMJSR) is proposed for the city environment [4]. Even in intelligent vehicles and sometimes for the design of assistance systems, driver's intention classification and identification is identified as the key technology. To study driver's steering intention under different typical operating conditions, five driving school coaches of different ages and genders were selected as the test drivers for a real vehicle test [5]. Furthermore, the knowledge of traffic flow and its modelling is relevant information that could be taken into account for decision-making in many intelligent systems. A localized space-time autoregressive (LSTAR) model is proposed and a new parameter estimation method is formulated based on the Localized Space-Time ARIMA -autoregressive integrated moving average- (LSTARIMA) model to reduce computational complexity for real-time traffic prediction purposes [6].