Driving Toy Car

Driving Toy Car

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A further 90, diesel vehicles will be banned from driving in Brussels from 1 January, as the region extends the criteria for its low emission zone (LEZ).The LEZ ban, which was first introduced in , will now be extended to cover the Euro 4 standard, meaning no diesel vehicles registered before January can travel within the Brussels region. This was .

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In order to make such decisions, the decision-making system needs to know the position of the car and its environment. It makes use of offline maps, sensor data, and platform odometry to determine the position of the car. The Route Planner module computes a route from the starting position to the goal defined. The path planner then computes a set of paths. A route is a collection of waypoints whereas a path is a collection of poses. The Behavior Selector module is responsible for choosing the current driving behavior, such as lane keeping, intersection handling, traffic light handling, etc.

The Obstacle Avoider module receives the trajectory computed by the Motion Planner and changes it typically reducing the velocity , if necessary, to avoid collisions. Finally, the Controller module receives the Motion Planner trajectory, eventually modified by the Obstacle Avoider, and computes and sends commands to the actuators of the steering wheel, throttle and brakes in order to make the car execute the modified trajectory as best as the physical world allows.

Combining inputs from multiple LIDAR modules around the car can be used to create an accurate map of its surroundings. After the invention of laser in , LIDAR was first tested on airplanes using downward facing lasers to map the ground surface. Radar was developed for the military back in s to detect aggressors in the air or on the sea. Aircraft and missile detection is still one of the main uses of radar. It is also widely used in air traffic control, navigation systems, space surveillance, ocean surveillance and weather monitoring.

For example, a Tesla has 8 cameras around the car which gives a degree view. This enables the Tesla vehicle to have full automation without requiring the help of other sensors. Recent advancements in deep learning and computer vision can enable self-driving cars to do these tasks easily.

The paper describes a convolutional neural network which is trained to map raw pixels from the camera feed to steering commands for the vehicle. Network Architecture The model consists of 5 convolutional layers, 1 normalization layer and 3 fully connected layer. The network weights are trained to minimize the mean-squared error between the steering command output by the network and the ground truth.

This convolutional neural network CNN has about 27 million connections and thousand parameters. Data Collection The training data is the image feed from the cameras and the corresponding steering angle. The data collection is quite extensive considering the huge number of possible scenarios the system will encounter. The data is collected from a wide variety of locations, climate conditions, and road types.

Also, training with data from the human drivers is not enough. The network should learn to recover from mistakes otherwise the car might drift off the lane. In order to solve this problem, the data is augmented with additional images that shows different positions where the car is shifting away from the center of the lane and different rotations from the direction of the road.

For example, the images for two specific off-center shifts from the left and right cameras and the remaining range of shifts and rotations are simulated using viewpoint transformation of the image from the nearest camera. The driver could be staying on lane, changing lane, turning and so on. In order to train a convolutional neural network CNN that can stay on lane, we take only the images where the driver is staying on lane.

Training In order to train the model, data from three cameras as well as the corresponding steering angle is used. The camera feeds and the steering commands are time-synchronized so each image input has a steering command corresponding to it. Training the neural network Images are fed into the CNN model which outputs a proposed steering command. The proposed steering command is then compared with the actual steering command for the given image, and the weights are adjusted to bring the model output closer to the desired output.

Once trained, the model is able to generate steering commands from the image feeds coming from the single center camera. The trained network is used to generate steering commands from a single front-facing center camera. Evaluation The trained model is evaluated in two steps, first in simulation and then in on-road tests. Using the simulation test, an autonomy score is determined for the trained model. The autonomy metric is calculated by counting the number of simulated human interventions required.

An average of 6 seconds is considered for a real life human intervention where they have to regain control of the vehicle and bring it back to the center of the lane. For the on-road tests, the performance metrics are calculated as the fraction of time during which the car is performing autonomous steering. Here is a video of DAVE-2 in action.

Next steps I hope you found this overview of self-driving car technology helpful. Time to heat up my solder iron! About Jaison Saji Chacko This post is part one of a four-part FloydHub blog series on building your own toy self-driving car. Jaison is a Machine Learning Engineer at Mialo. He is based in Bangalore, India. You can follow along with Jaison on Twitter and Github.

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