Entries by tsc_admin

Understanding human driver behaviour

By Ross Walker, Research Fellow in Autonomous Cars, Cranfield University   The Multi-Car Collision Avoidance (MuCCA) Project will develop a next-generation driver aid that aims to avoid multi-car collisions on motorways – if an accident cannot be avoided, the MuCCA system will attempt to minimise its consequences (both injuries and damage). But before the MuCCA vehicles are able to react to these potentially dangerous situations, they have to understand how a human driver would react. And to do so, we need to develop a training algorithm that predicts how drivers behave when avoiding collisions.   Accident prevention Drivers avoid accidents by reacting fast enough to a dangerous situation, however for multiple car collisions simply reacting may not be enough. By modelling how human drivers behave on motorways, and how the proximity of surrounding cars influence their behaviour, the movement of the cars that surround the MuCCA vehicle can be predicted over the next few seconds. This can allow any potential accidents to be recognised in advance, and consequently avoided before they have chance to begin developing.   Data acquisition To predict potential accidents we first need to learn the driver trajectories that result in them. To safely obtain such trajectory data we use simulation software so that accidents can be created within a virtual (safe) environment.   Tailoring scenarios to the MuCCA Project The MuCCA project deals with up to five vehicles moving along a passage of UK motorway; the prediction algorithm adopts the same parameters, and various training scenarios are devised so that meaningful human driving behaviour can be captured in the simulator.   To begin, a simple test should always be carried out when initially developing an algorithm – this is to ensure results are as expected and the algorithm is behaving correctly. A simple behaviour was captured from a single vehicle having to pass by a broken down car in their lane. This was primarily to gauge if the driver’s reaction time to the blocked lane replicated a real-life scenario. The introduction of multiple cars allowed us to capture data on how well the drivers maintained motorway lane discipline. It also gave insight into how drivers undertake, lane-hog, or tailgate, which all increase the likelihood of a collision. a) Which lane will the leading HDV move to? b) How will the back HDV cope with the front HDV moving in front? c) What will the right HDV […]

Using a Systems Engineering approach

Multi-Car Collision Avoidance Project (MuCCA) is a complex, collaborative R&D project that can benefit greatly from a systems engineering approach, says Thomas Levermore, a Systems Engineer at the Transport Systems Catapult (TSC). The MuCCA project will develop a next-generation driver aid that aims to avoid multi-car collisions on motorways – if an accident cannot be avoided, the MuCCA system will attempt to minimise its consequences (both injuries and damage). This is a complex project by any definition, and by applying systems engineering methods, we can introduce technological innovation into the planning and development stages. The starting point of the systems engineering process is pulling together the requirements of the stakeholders. The MuCCA project brings together six partners, each contributing to the common goal of reducing the number and severity of multi-car collisions. However, with partners coming from academia, small, medium and large companies, each has a different approach and existing technical systems to incorporate. Before all the partners begin to develop their parts of the MuCCA system, systems engineering ensures everyone takes a step back, looks at the system as a whole in order to understand its purpose and where their elements of the system fit with others. As a systems engineering team at TSC we provide neutral guidance to the system design to balance each partners’ objectives and ensure the system meets the needs of future users. By developing an operational concept for the system, all the assumptions and constraints can be documented and a definition of what the system will (and importantly won’t) do can be agreed upon. Fostering agreement To reach an agreement on the scope of the MuCCA prototype system, use cases were developed along with the operational concept to describe situations in which the system is to intervene. These use cases were described by the approximate relative starting positions and speeds of the cars involved. Examples are two side-by-side cars avoiding a crashed vehicle, and a car avoiding a crash ahead which is partially obscured by the car in front. >  From this foundation, requirements were discussed with the consortium to detail what the system is required to do so that it can achieve the objectives of the project. >  With an operational concept and an initial set of requirements, the v-model process followed in this project leads to development of the system architecture. Figure 1: V-model Systems Engineering process   Defining an architecture A cornerstone of any successful systems engineering process is a well-defined system architecture. At the mention of system architecture, it is common to imagine a diagram of how physical pieces of equipment fit together. Often when existing components are available to be used in a system, there is a tendency to work backwards from what is available to dictate what the functionality of the system can be, resulting in a limited system. Going straight to the physical architecture misses a vital step in the process. Taking a step back and looking at the functionality needed to satisfy the requirements allows a functional architecture to be developed […]

Decision making algorithms

MuCCA cooperative path-planning/decision making algorithms and its applications on motorways Are we ready for autonomous driving? How autonomous driving can assist humans on the motorways? These issues are recurrent nowadays in the face of the biggest challenges that are appearing to building safety systems on our roads. Autonomous driving systems can help decrease fatalities caused by traffic accidents. However, this technology has a good deal of aspects to be improved so as planning obstacle-free paths for a vehicle to increase highway safety.   A challenging research task for autonomous driving is the coordination of several autonomous cars. This new technology is the guarantee of making car traffic safer in the future. Cooperative systems have just recently started to be explored in the context of automated driving and are expected to become increasingly common on motorways. In this context, the MuCCA (Multi-Car Collision Avoidance) project, funded by the Centre for Connected and Autonomous Vehicles (CCAV) via Innovate UK, is developing a cooperative system that will enable connected and autonomous vehicles (CAVs) to avoid collisions. As part of this project, Cranfield University is responsible for developing robust algorithms for path prediction and decision-making/inter-vehicle communication.   The actual version of the algorithms developed considered a strategy based on model predictive control (MPC) to make cooperative decisions to avoid a potential accident. We explored an extending cooperative path-planning approach to traffic where human drivers are present in on-road scenarios. In the not-too-distant future, it is feasible that cooperative solutions to driving manoeuvres that would enable autonomous vehicles to simultaneously coexist on the roads with vehicles driven by humans. In this context, the human driver model (HDM) needs to be integrated with the autonomous planning in order to safely interact with the autonomous vehicles on the road.   Our cooperative planning framework re-plans the route of the vehicle for each step and can be summarized in Figure 1. This solution is able to provide the following improvements and capabilities:   Incorporation of constraints for smoother manoeuvring when avoiding obstacle. System constraints are the set of rules to drive on roads, road boundaries and logical propositions that guarantee a safe distance to other MuCCA vehicles and obstacles. Increased prediction horizon for path planning and more short horizon for trajectory control in order to get better predictions and precise vehicle control. Demonstrates V2V communications to avoid collisions. The plans of other vehicles can be communicated in real […]