The research presented in this chapter focused on identification of macro-terrain features for the purpose of vehicle speed adaptation. The aim was to develop a robust, real-time method that can perform in real-world conditions. This work has developed an image processing framework for terrain geometry identification with particular focus on detection of crest and ditches and slope measurement. It has been shown that the proposed method is effective in identifying ditches and crest with accuracy of distance estimation of 0.5 m. Crests were detected up to 18 m, whereas ditch detection range highly depends on the ditch geometry, depending on the ditch size. The slopes were shown to be measured with accuracy of +/- 2.5˚ in controlled environment on concrete objects.
The proposed system was also validated in conjunction with TBSA in off-road environments showing that the production representative sensors and the proposed method can robustly identify terrain geometry in real-time at 10 fps. In conclusion, the proposed solution addresses a known deficiency of the reactive TBSA system by pre-emptively identifying macro terrain features allowing the vehicle to adjust the speed before reaching the terrain.
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8
Pre-emptive speed recommendation using
surface roughness
This chapter addresses the question of how to identify surface roughness, which have been identified in Section 5.2 as a crucial element for the pre-emptive perception system for TBSA. As discussed in Section 5.2 the surface roughness measurement should allow the system to identify transitions between surfaces of different roughness and discrete micro terrain features such as kerbs, rocks and logs which will not be identified as macro terrain features. The purpose of identifying surface roughness is to recommend the vehicle speed to achieve a target level of passenger comfort based on the vibration levels experienced by the vehicle as discussed in Section 5.2. Since the relationship between the pre-emptively measured surface roughness and target vehicle speed was unknown, this work also addresses the problem of mapping the proposed roughness metric to target vehicle speed.
Literature in the domain of off-road perception and controls focuses on custom build vehicles and robots with extensive suite of sensors such as lidars and cameras. These systems are designed to operate autonomously hence the focus of perception is to create the world model, describing hazards so the vehicle can avoid them. Adjusting the vehicle speed based on pre-emptive information is either simplified to choosing the speed level based on the pre-defined terrain class [149], using a previously acquired map of the environment with speed limit levels or mapping terrain traversability into vehicle speed. The proposed approaches do not consider the levels of vehicle vibration that will be excerpt on the vehicle but rather try to minimise the risk of severe damage to the vehicle by planning conservative routes. To the author’s knowledge adjusting the vehicle speed pre-emptively to maintain passenger comfort has not yet been achieved, both within the realms of automotive industry or the autonomous vehicle research community.
The study presented in this chapter analyses the performance of the method for assessing surface roughness from the remote sensed data on the vehicle and the ability of surface roughness descriptors to distinguish between different rough surfaces. Many different methods are proposed in the literature to assess road surface roughness, but most of them were used to assess the condition of the roads for maintenance or to produce simulation data for vehicle durability testing, and only a small number of methods were used for the purpose of speed adaptation. The roughness descriptors proposed for the purpose of speed adaptation however do not take into account passenger comfort but focus on limiting excessive shock experienced by the vehicle [170]. This work presents a method that enables the vehicle to acquire a roughness measurement for vehicle speed selection taking into account the passenger comfort.
The core contribution of this study is the design and implementation of a novel pre-emptive speed recommendation system that allows the vehicle to adapt the speed dynamically based on the roughness
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of the surface ahead. The proposed surface roughness descriptor was used for characterisation of road networks roughness and simulations of the vehicle reliability, but was not used for characterisation of the short surface intervals especially for the purpose of speed adaptation. This work shows novel use of this roughness descriptor and both experimentally and analytically validates its use for the purpose of speed adaptation.
The proposed solution has been implemented on a production ready sensors, showing that the system is feasible to be introduced in the next generation of Land Rover vehicles. Extensive experimental validation quantifies the benefits of employing the pre-emptive system to reduce the number of events causing extreme passenger excitation to compliment the performance of the purely reactive system. In contrast to previous research, the system was designed with limited capability commercially available automotive sensor and the performance of the system was extensively assessed on long stretches of various surfaces showing that the system is capable of adapting the vehicle speed within range of +/- 5 kph.
Section 8.1 introduces the proposed processing pipeline. Section 8.2 describes method for acquiring surface profile. Section 8.3 introduces the training data set used to derive the roughness descriptors and velocity mapping.
Sections 8.4 and 8.6 present the proposed surface roughness descriptor and the speed recommendation methods including a machine learning and a direct functional mapping approach. The roughness descriptors are evaluated qualitatively on a range of different surfaces showing the correlation of the roughness measurement from the stereo camera and the acceleration levels linked to passenger comfort. The speed recommendation performance is analysed on these surfaces with respect to an expert chosen target vehicle velocity (Section 8.7).
Section 8.8 presents an extensive evaluation of the pre-emptive speed recommendation system in contrast with a purely reactive system. The performance is analysed in terms of the potential improvements of speed selection while approaching terrain events that cause discomfort when approached too rapidly by the reactive system while still maintaining speeds similar to the reactive system when the comfort metric is within the acceptable limits to ensure that the overall progress of the vehicle is not hindered.