Samenvatting

In 2017 Volvo will deliver 100 self-driving cars as part of the Drive Me project. These cars will be able to drive autonomously on the ring road of Gothenburg, without requiring any driver supervision. To realize this vision, the cars need different algorithms to recognize, and be able to react to, different traffic situations. One of these traffic situations is when a car unexpectedly cuts in, in front of the self-driving car. For the self-driving car to react to the cut-in, it first needs to detect the cut-in.

The main question of this research is: Is it possible to detect a cut-in by the use of a neural network? A neural network is a network with the ability to learn without being explicitly programmed. The neural network is provided with examples of a cut-in and examples of a non-cut-in and learns to recognize these examples. The neural network that is being used to detect a cut-in is provided with 70 samples of each cut-ins and non-cut-ins. In this thesis, the optimal conditions to train this neural network are examined.

The input variables used to create the cut-in samples are determined. The four parameters chosen as input variables to the neural network are the lateral position, lateral velocity, and trajectory angle of the car making a cut-in with respect to the road, and the width of the lane that the host vehicle is driving upon. These parameters contain 40 measuring points per second. The Research carried out in this thesis shows that this can be reduces to 20 measurements per second without influencing the performance of the neural network. This benefits the memory use of the neural network.

The detection of a cut-in is most useful when the cut-in is detected before it actually happens. This way the host vehicle has time to react to the situation. The usability of the network increases the earlier that a cut-in can be detected. The requirement states that a car executing a cut-in must be detected at least 4 seconds before the car crosses the lane marker. When the previous described input variables and this time interval of 4 seconds are used to train the neural network, it can reach a performance of 90.5%. This means that 90.5% of the cut-ins or non-cut-ins are correctly identified. This is considered to be a well trained neural network. Because the network becomes more useful for a shorter time interval, a time interval of 3 seconds is tested. This time interval starts 4 seconds before the car crosses the lane-marker and ends 1 second before the car crosses the lane marker. The network reaches a performance of 57.1%. This is a useless result, so this time interval is too small. The last interval tested is 3.5 seconds. The interval starts 4 seconds before the car crosses the lane marker and stops half a second before the car crosses the lane marker. This network reaches a performance of 81.0%. This is an acceptable result, but it is significantly lower than the time interval of 4 seconds. Depending on the preference, one could choose the time interval of 4 or 3.5 seconds. If a secure detection is chosen to be more important, it is wise to choose the time interval of 4 seconds. If a fast detection is chosen, one could choose the time interval of 3.5 seconds.


There are a lot of different algorithms that can be used to train a neural network. This specific neural network used to detect cut-ins, is trained with the Levenberg-Marquardt backpropogation algorithm.