In this chapter the method which enables the vehicle to robustly estimate roughness to adjust the vehicle speed is presented. The diagram of the processing pipeline is presented in Figure 8.1. From the Multi Level Surface (MLS) height map and the predicted vehicle path as introduced in section 7.1, the Power Spectral Density (PSD) of the surface height profile is calculated. PSD is then processed by a mapping
Engineering Doctorate Programme Page 84
function which assigns a recommended vehicle velocity based on the surface roughness described by the PSD.
Speed recommendation
Disparity 3D point colud MLS
Surface profile Predicted path Steering input PSD Recommended velocity DFM/ANN
Figure 8.1. Pre-emptive speed recommendation processing pipeline.
The presented method is based on PSD of the surface profile frequency spectrum as discussed in Section 3.7.2. This approach incorporates instantaneous surface profile measurements ahead of the vehicle with the predicted vehicle path to relate the future state of the vehicle with the scene ahead. The key of this approach is that PSD can be directly mapped onto recommended vehicle speeds as PSD represents energy content of the surface profile. The roughness estimator is then used to establish the recommended vehicle speed. This is achieved through two distinct approaches, one using machine learning with Artificial Neural Networks (ANN) [197] and another using a regression model which in this work will be referred to as Direct Functional Mapping (DFM). Training data is obtained by driving through a representative set of surfaces with the target speed assigned by an expert. This acts as a control signal that is used for training as a reference. This data is then used both to train a supervised classifier which is able to predict the vehicle speed and to create a direct mapping function between roughness descriptor and the target vehicle speed. These two approaches were developed in parallel in order to understand which of these would be a more suitable approach. The learning approach should be able to transfer the capability of sensing the surface roughness from the profile ahead to the appropriate vehicle speed.
The method is evaluated on the data sets consisting of different surface samples with varying roughness levels. The experiments measure the ability of the surface roughness descriptor to distinguish between different surfaces and also the ability to recommend the vehicle speeds on homogenous surface types. The method is derived in three stages. First the surface roughness measurement is derived showing the capability of distinguishing different surfaces based on PSD roughness metric (Section 8.5.1). Secondly,
Engineering Doctorate Programme Page 85
this relationship is used to build a mapping function for establishing appropriate vehicle speed purely based on the pre-emptive measurements taken with a stereo camera (Section 8.6.3). The relationship between the target speed and surface roughness is then derived.
8.2 Dataset
The set consists of 50 m straight segments of homogeneous surfaces to decouple the effect of varying conditions of the surface as well as the sampling of the surface profile on non-straight paths. It is crucial that the dataset consists of homogenous surfaces in order to establish the correct mapping between the surface roughness descriptor and the chosen target vehicle speed.
The selection of surfaces chosen covers a range of surfaces with varying roughness types requiring different vehicle speeds. The target vehicle velocities for each of the terrain samples were chosen by the
off-road experts as safe and comfortable velocities that the vehicle should achieve on these surfaces1.
The overview of selected surfaces constituting the dataset is summarised in Table 8.1 with example images presented in Figure 8.2. The selection in this section is also limited to exemplars which are used for creating the speed mapping, whereas a more extensive data set is used for the pre-emptive speed recommendation system validation against the reactive speed recommendation system in Section 8.8.
Set Target speed [kph] Example image Description
riverbed 3 a Extremely rough, rocky surface, any velocity above 5kph will cause vehicle damage
tarmac 302 b Smooth tarmac surface
dirt 1 30 c Smooth dirt track with potholes
dirt 2 30 d Smooth dirt track
perimeter
1 20 e Relatively smooth surface with small rocks
cross
country 1 15 f
Track consisting of dirt and small rocks with undulations and large potholes
cross
country 2 10 g
Track consisting of dirt and small rocks with undulations and large potholes
Table 8.1. Surface roughness dataset
Riverbed data set is the most challenging terrain consisting of large rocks and demanding very low vehicle speed of 3kph. Tarmac, dirt 1, dirt 2 are the smoothest surfaces allowing maximum vehicle speed of 30 kph. Perimeter 1 is a relatively smooth surface with potholes and small rocks requesting slower speeds of
1 Personal communication with JLR Peerless Effortless All Terrain team, June 2015
2 Maximum system allowable speed due to system limits. Higher vehicle speed can also be recommended for
Engineering Doctorate Programme Page 86
20 kph. Cross country are slightly rutted tracks with undulation and potholes hence the requested vehicle speed is between 10 and 15 kph.
Figure 8.2. Example images representing data set summarised in Table 8.1.
(f) (e) (d) (b) (a) (c) (g)
Engineering Doctorate Programme Page 87