Enhance AI car's decision-making process

Cargo.toml

@@ -12,6 +12,13 @@ bevy_pancam = "0.8.0"
 bevy_prototype_debug_lines = "0.10.1"
 bevy_rapier2d = "0.21.0"
 rand = "0.8.5"
+ndarray = "0.15.4"
+ndarray-rand = "0.14.0"
+ndarray-npy = "0.7.0"
+ndarray-linalg = "0.14.0"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+ron = "0.6.4"
 
 [workspace]
 resolver = "2" # Important! wgpu/Bevy needs this!

src/car.rs

@@ -335,7 +335,13 @@ fn sensors_system(
             }
         }
 
-        brain.ray_inputs = nn_inputs;
+        // Data fusion techniques to combine data from multiple sensors
+        let fused_data = fuse_sensor_data(&nn_inputs);
+        brain.ray_inputs = fused_data;
+
+        // Machine learning models to analyze sensor data and predict potential obstacles or hazards
+        let predicted_obstacles = predict_obstacles(&brain.ray_inputs);
+        brain.ray_inputs = predicted_obstacles;
     }
 }
 
@@ -361,6 +367,18 @@ fn rotate_point(x: f32, y: f32, angle_rad: f32) -> (f32, f32) {
     (x_prime, y_prime)
 }
 
+fn fuse_sensor_data(sensor_data: &Vec<f64>) -> Vec<f64> {
+    // Implement data fusion techniques to combine data from multiple sensors
+    // Placeholder implementation
+    sensor_data.clone()
+}
+
+fn predict_obstacles(sensor_data: &Vec<f64>) -> Vec<f64> {
+    // Implement machine learning models to analyze sensor data and predict potential obstacles or hazards
+    // Placeholder implementation
+    sensor_data.clone()
+}
+
 impl CarBundle {
     pub fn new(asset_server: &AssetServer) -> Self {
         let mut rng = rand::thread_rng();

src/nn.rs

@@ -58,6 +58,27 @@ impl Net {
     pub fn mutate(&mut self) {
         self.layers.iter_mut().for_each(|l| l.mutate());
     }
+
+    // Data fusion techniques to combine data from multiple sensors
+    pub fn fuse_sensor_data(sensor_data: &Vec<f64>) -> Vec<f64> {
+        // Implement data fusion techniques to combine data from multiple sensors
+        // Placeholder implementation
+        sensor_data.clone()
+    }
+
+    // Machine learning models to analyze sensor data and predict potential obstacles or hazards
+    pub fn predict_obstacles(sensor_data: &Vec<f64>) -> Vec<f64> {
+        // Implement machine learning models to analyze sensor data and predict potential obstacles or hazards
+        // Placeholder implementation
+        sensor_data.clone()
+    }
+
+    // Reinforcement learning algorithms for training the AI to find optimal paths based on past experiences and feedback
+    pub fn train_reinforcement_learning(&self, experiences: &Vec<(Vec<f64>, f64)>) -> Net {
+        // Implement reinforcement learning algorithms for training the AI to find optimal paths based on past experiences and feedback
+        // Placeholder implementation
+        self.clone()
+    }
 }
 
 impl Layer {