Augmenting End-to-End Steering Angle Prediction with CAN Bus Data
Rohan Gupta
In recent years, end to end steering prediction for autonomous vehicles has
become a major area of research. The primary method for achieving end to end
steering was to use computer vision models on a live feed of video data.
However, to further increase accuracy, many companies have added data from
light detection and ranging (LiDAR) and or radar sensors through sensor fusion.
However, the addition of lasers and sensors comes at a high financial cost. In
this paper, I address both of these issues by increasing the accuracy of the
computer vision models without the increased cost of using LiDAR and or
sensors. I achieved this by improving the accuracy of computer vision models by
sensor fusing CAN bus data, a vehicle protocol, with video data. CAN bus data
is a rich source of information about the vehicle's state, including its speed,
steering angle, and acceleration. By fusing this data with video data, the
accuracy of the computer vision model's predictions can be improved. When I
trained the model without CAN bus data, I obtained an RMSE of 0.02492, while
the model trained with the CAN bus data achieved an RMSE of 0.01970. This
finding indicates that fusing CAN Bus data with video data can reduce the
computer vision model's prediction error by 20% with some models decreasing the
error by 80%.