Please forgive the naivity of this question, it's just due to lack of experience. It goes without saying that self-driving cars have up to 8 cameras and more that do various vision related tasks: object detection and localization, 2D to 3D depth perception, semantic segmentation, and probably more that I have yet to learn about. My question is on the synchronization of these cameras: I am assuming there's a hardware sync to ensure all inputs are captured at the "exact …

I am writing a research paper and I am looking for reliable sources that provide information on disasters of machine learning. Especially in the field of autonomous driving. Have there been any incidents when something went really wrong? Any links to articles or research papers would be helpful.

Are there any existing methods/models describing the probability of an object being detected by a computer vision algorithm given it is seen $n$ times at similar angles and orientations? I know that an autonomous car may, for example, have trouble recognizing a stop sign and as a result the image recognition system being used may have a bounded box around the stop sign continuously appearing and disappearing signaling that the object recognition algorithm is only detecting the stop sign a …

So I'm trying to implement Nvidia's end to end driving paper to simply have an agent in Carla follow the lanes. I'm trying to predict the steering angle of the car based on the RGB images from the front camera. I'm getting and r_score of about 1.20 % Every image in the dataset has a steering angle. So here are plots for the distribution of steering angles. I've also augmented the images but when I run it, the car still …

I'm trying to make a self-driving AI that can drive around in GTA-San Andreas by following sentdex's videos on making a self-driving AI for GTA-V but my model always starts to overfit after 4 epochs or so. Even after doubling the dataset that I originally had, the model is still overfitting. The dataset made up of grayscale images (270x360) and is distributed into 250 .npy files with 1000 frames each totaling 250,000 frames. I'm using Xception architecture without any pre-trained …

I'm planning to build a small car with autonomous driving (maybe modifying my current rc car or using a robot car kit, using arduino and raspberry). I'll use a CNN, and I'm thinking how to collect data (I want to try a similar approach to the Udacity simulator). My doubt is if is better to aim at supervised learning or reinforcement learning. I'm more inclined to the supervised learning, but I don't know the best way to record data. If …

I have set of night images which I will be using for self driving. But I want to convert those images into day images. I have developed algorithm based on day image but it is not good for night images , so I want to convert night images to day images then feed into the network. As far as I have explored image colourization techniques of grey scale image ( converting night image to black and white and then coloring …

I am working on 3D object detection in the context of self driving cars and I was wondering if there are other challenging benchmarks for this beside Kitti.