Deep Learning for Autonomous Driving: A Breakthrough in Urban Navigation

Self-driving vehicles have six different levels of autonomy, from drivers being in full control to full automation. According to Statista, the market for autonomous vehicles in levels 4 and 5 will reach $60 billion by 2030.

Countries and automobile companies around the world are working on bringing a higher level of unmanned driving to a wider audience. South Korea has recently announced it is to invest around $1 billion in autonomous vehicle technologies and introduce a level 4 car by 2027.

Machine learning and deep learning are among other technologies that enable more sophisticated autonomous vehicles. Applications of deep learning techniques in self-driving cars include:

• Scene classification
• Path planning
• Scene understanding
• Lane and traffic sign recognition
• Obstacle and pedestrian detection
• Motion control

Deep learning for autonomous navigation

Deep learning methods can help to address the challenges of perception and navigation in autonomous vehicle manufacturing. When a driver navigates between two locations, they drive using their knowledge of the road, how streets look like and traffic lights, etc. It is a simple task for a human driver, but quite a challenge for an autonomous vehicle.

Here at ELEKS, we’ve created a demo model that can help vehicles to navigate the environment as humans already do – using eyesight and previous knowledge. We came up with a solution that offers autonomous navigation without GPS and vehicle telemetry by using modern deep learning methods and other data science possibilities.

We used only an on-dash camera and street view dataset of the city of Lviv, Ukraine; we used no GPS or sensors. Below is an overview of the techniques applied and our key findings.

1. Image segmentation task

We used a Cityscapes dataset with 19 classes, which focuses on buildings, road, signs, etc., and an already trained model from DeepLab. The model we used is based on Xception inference. Other models with different maps/IoUs are also available.

Final layers were the semantic probabilities — mostly dim ~ classes*output_image_dims (without channels), so they could be filtered and become the inference to similarity model. It is recommended to transform them into the embedding layer or find a more suitable layer before the outputs. However, even after transformation objects position (higher or lower) on the frame and distance to it, may have influenced the embedding robustness.

2. Gathering additional data and labelling

We then downloaded raw photos from the web of the streets, road names and locations (coordinates, etc.), and we also got the Street View API key for download. We added labels in semi-automated ways based on the names and locations and verified them manually. We created pairs of images for similarity model training.

Finally, we used the image augmentation (also adding photos of different times of day and seasons) and image labelling using model (for example, additional negative samples, which the model recognizes as similar, but they are not located on the one street (GPS, street names, etc.)). As a result, we created a dataset containing approximately 8-12K augmented images.

3. Similarity models ideation and validation

We tested a few streets view comparison approaches from classical descriptor and template matching to modern SOTA DL algorithms like QATM. The most accurate was the inference model with representation for each segmented image in a pair, like VGG, ResNet or efficientNet and binary classifier (xgb or rf). Validated accuracy equals to approximately 82.5% (whether the right street was found or not), taking into account Lviv’s most known streets between 2011 and 2019 and with augmentation (changing image shapes, lightning, etc).