A new £4.6m (US$6.3m), 30-month project in the UK will develop a next-generation driver aid that aims to avoid multi-car collisions on motorways, and if an accident cannot be avoided, the system will attempt to minimize its consequences.
Led by Applus+ Idiada UK and with financial support from the CCAV (Center for Connected and Autonomous Vehicles) and Innovate UK, the MuCCA (Multi-Car Collision Avoidance) project is developing a collaborative system that will enable connected and autonomous vehicles (CAVs) to avoid collisions.
At proof-of-concept demonstrations in late 2019, MuCCA-equipped cars will communicate with each other in the fractions of a second before a potential crash, and agree and act upon the best course of evasive action for each individual vehicle to take.
Although CAVs are expected to become increasingly common over the next few years, many ‘human driven’ vehicles will remain for the foreseeable future, so the system will also take on the added complexity of anticipating the likely behavior of any human drivers in the vicinity of the potential crash.
The test-track demonstrations will be based upon a typical motorway scenario, and the environment is being designed to incrementally explore any limitations of the system. The tests will initially be conducted only in daylight and in reasonable weather conditions, with no curves or junctions on the sections of track being used. Experienced test drivers will pre-position the cars in controlled positions and trajectories before handing them over to computer control. Provided by project partners Westfield Sportscars, all the vehicles will be of a similar type, and the trials will begin with very simple scenarios and low speeds before progressing to more complex and higher speed trials as the system is proven capable.
While the involvement of expensive prototype vehicles and human drivers places some limitations on the risks that can be managed on the track, there are no such restrictions in laboratory tests. Simulation trials will also be used to create scenarios where collisions cannot be fully avoided, with the connected cars instead looking to minimize the severity of crashes.
A different type of simulator will also be used for gathering much of the initial data that will generate the ambitious Human Driver Model (HDM) being developed by Cranfield University, which will help the system predict the most likely path that the conventionally driven vehicles will take.
This will involve five interlinked driving simulators, so that the behavior of several drivers can be captured simultaneously, which will share a single virtual environment with additional virtual cars. Several collision or near-collision scenarios will capture the actions taken by a varied pool of human test subjects.
In both simulated and track environments, the MuCCA system will need to demonstrate its ability to take in multiple data sources from the sensors of various connected vehicles, recognize potential collision risks as they occur, determine the best course of action, and then transmit that shared plan to all vehicles in the vicinity. The other consortium members are: Secured By Design (cybersecurity) and Cosworth (data fusion and logging).