Collisions involve two road users attempting to occupy the same space on the road at the same time. Many accidents involving motorcycles are collisions with other road users, where the rider was taken by surprise but the collision was otherwise both commonplace and avoidable. The Cango?-Willgo! concept explains collisions in terms of prediction failure rather than the commonly-accepted explanation of rule-breaking or judgment failure. Cango?-Willgo! further extends the basic principle of No Surprise: No Accident.
Avoiding collisions involves making predictions about how other road users will move in our immediate vicinity, but just how we make those predictions is an area that’s undergone a radical rethink in recent years.
In the traditional view of the brain, it was thought that our brains always process the world around us from the ‘bottom up’ by absorbing information and processing it in a linear fashion, in such a way that we derive meaning and understanding. It was thought our brain would process the visual signals, then calculate paths and velocities and finally work out whether there was a risk of a coming-together. When accidents occurred, they were frequently explained in terms of deliberate breaking of rules designed to keep road users safe, or as failures of judgment by those road users leading to poor decisions, sometimes ending up with a loss-of-control crash.
In the last decade or so, many neuroscientists have shifted to a ‘top-down’ view of the brain, which they now see as a ‘prediction machine’; rather than process information in a linear, deductive fashion, we absorb the picture, compare what we see with what we’ve seen happen before, then guess what will happen next based on the closest-matching, most commonly occurring scenario in our memory. The error then becomes one of prediction; it turns out that what we thought was happening actually isn’t. The sudden surprise when we discover that the situation developing is not the same one we predicted frequently triggers the brain’s instinctive and inappropriate responses to danger, including target fixation, freezing or excessive inputs to the controls. An avoidable collision becomes unavoidable.
How do we do make predictions? It all begins with movement. The brain is highly evolved in terms of detecting and understanding movement.
The movement of all objects, ourselves included, is governed by Newton’s ‘Laws of Motion’. Whilst few people would be able to define those laws, by watching what happens our growing experience gives us a good intuitive grasp of how the laws of motion apply to objects and allows us to accurately predict how those objects will move in the future. For example, with experience the motion of traffic becomes predictable; cars mostly go where their front wheels are pointing, big heavy trucks only manage to accelerate slowly and nothing stops instantly unless it hits something or something else hits it.
But not only can we predict the motion of vehicles and other road users and when they might get there, we can also predict where they are likely to move to. The nature of a land-based vehicle is that it can only move forwards and backwards, or combine forward and backward movement with a turn to the left or right. Freedom of movement is constrained by the roadway itself, the presence of other road users and the presence of other obstacles. For example a car parked in a driveway nose-in to a wall is constrained in where it can move by the wall. If we see a driver climb into the car and start the engine, the driver could move forward and collide with the wall but experience tells us that the prediction in which we can have far more confidence is that the driver will reverse backwards away from the wall.
A third factor is that our predictions encompass the dimension of time. If the driver begins to reverse towards a road, then we can also predict that a moment later he will turn as he reverses, either to the left or the right. If the parking space is a bit tight because of cars parked on the other side of the road, then we can predict that the driver might have to go backwards and forwards a number of times before they have wriggled themselves enough room.
Whilst it’s true that the further we try to look into the future, the more rapidly the potential range of choices available to those road users multiplies, the nature of collisions means that we actually only have to deal with a relatively limited number of possibilities. That’s because there is usually only a small window where a prediction failure will actually result in a collision. Collisions will only occur when two road users try to occupy the same space at the same time. In the example of the reversing car, a collision will only occur if another road user attempts to pass behind it at just the wrong moment.
Sometimes collisions result from simultaneous movements to occupy a single space – for example, collisions between vehicles changing lanes on multilane roads, collisions with turning vehicles at junctions – or someone colliding with our reversing car. We can conclude that both parties to the collision got their predictions wrong and neither party predicted that the other road user would try to occupy the space they had already allocated to themselves. In the case of the most common collision involving a motorcycle, that involving an emerging car at a junction, the precipitating action might by the failure of the car driver to predict that there would be a motorbike occupying the space that they were about to bag either because they hadn’t seen it or had misjudged the motorcycle’s ‘time to arrival’. But a contributory factor is that the motorcyclist had incorrectly predicted that there wouldn’t be a car in the space he was about to ride into!
Other collisions occur when vehicles attempt to occupy a space already taken up by another vehicle – classic examples are collisions with the back of stationary queues, drivers and riders who change lanes having failed to notice another vehicle in their blind spot – or our reversing driver colliding with an awkwardly parked car. Motorcycles are easily missed in the mirrors and in the example of a driver changing lane, if they haven’t seen the bike, we won’t ‘exist’ in the mind of the other road user. The space we’re actually occupying would appear to be ’empty’, and thus would provide somewhere the other road user could move to. Thus the precipitating action might be the failure to look properly but a contributory factor is a failure to predict that another road user could try to occupy the space we’re already in!
So if these movements are easy to predict, why do so many road users fail? One possible reason is that we’re taught that riders and drivers should adhere to ‘the rules’. We assume “Can go? Won’t go” because the driver isn’t supposed to and usually doesn’t. Mistakes are thus seen as extra-ordinary events, and ones for which learning to drive or ride leaves us largely unprepared, rather than seeing “Can go? Will go” mistakes as everyday events that we need to prepare for. Riders in Japan are taught from the very beginning that spaces and gaps are just as important as solid objects like cars and people and perhaps that is why their accident rate is so much less than ours.
The key to the Cango?-Willgo! idea is thus for the motorcyclist to focus on predicting the POTENTIAL movement of other road users rather than relying on what they SHOULD do. By understanding how the laws of motion could allow them to move in space and time, and how they are constrained by their own environment, a rider should be able to accurately predict the potential for another road user to move in a way that conflicts with the rider’s own choices. Put simply, Cango?-Willgo! involves determining if there is an opportunity for another road user to move into the space we already occupy or are intending to occupy, and thus avoid being surprised. No Surprise? No Accident!
Cango?-Willgo! It will save your life.