https://github.com/michaelinzo/crashscope-ai

"Advanced AI to analyze traffic incidents, providing rapid, precise accident reconstruction using Gemini 1.5 pro technology."
https://github.com/michaelinzo/crashscope-ai

Last synced: about 2 months ago
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"Advanced AI to analyze traffic incidents, providing rapid, precise accident reconstruction using Gemini 1.5 pro technology."

• Host: GitHub
• URL: https://github.com/michaelinzo/crashscope-ai
• Owner: MiChaelinzo
• License: cc0-1.0
• Created: 2024-04-30T16:27:53.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-05T16:46:28.000Z (about 1 year ago)
• Last Synced: 2025-01-21T05:07:44.741Z (4 months ago)
• Language: Python
• Size: 1.32 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CrashScope-AI

"Advanced AI to analyze traffic incidents, providing rapid, precise accident reconstruction using Gemini 1.5 pro technology."



Explanation:

- sense_environment(): This function simulates sensor readings. In a real system, it would gather data from various sensors like cameras, lidar, and radar.

- make_decision(): Based on the sensor data, this function implements a simple decision-making process. If an obstacle is detected, it instructs the car to brake. If the car is departing from the lane, it instructs to steer. Otherwise, it instructs the car to accelerate.

- control_car(): This function simulates car control actions based on the decision made. In a real system, it would interface with the vehicle's control systems to execute the desired actions.

Limitations:

- This script is highly simplified and lacks the complexity of real-world autonomous driving scenarios.

- It does not incorporate learning or adaptation capabilities.

- Sensor fusion and advanced decision-making algorithms are necessary for robust performance.

Improvements:

- Risk Assessment: The assess_risk function calculates a risk factor based on the distance to the obstacle and the car's position within the lane. Closer obstacles and larger deviations from the lane center increase the risk factor.

- Decision Making with Thresholds: The make_decision function uses risk thresholds to determine the appropriate action. Higher risk values trigger braking, while moderate risk prompts steering adjustments.

- Steering Direction: The control_car function now considers the lane position to determine the direction of steering (left or right) for corrective action.

Additional AI Considerations:

- Machine Learning: This script could be enhanced by incorporating a machine learning model trained on real-world driving data to predict risks and make more nuanced decisions.

- Sensor Fusion: Integrating data from multiple sensors, such as cameras, lidar, and radar, would provide a more comprehensive understanding of the environment and improve risk assessment.

- Adaptive Behavior: The car's behavior could adapt based on factors such as road conditions, traffic density, and driver preferences.

Key Enhancements:

- Relative Speed: The script now considers the relative speed of the obstacle, influencing both risk assessment and prediction.

- Predictive Element: The predict_future_risk function estimates future risk based on current risk and relative speed. This allows the car to anticipate potential collisions and react proactively.

- Adaptive Decision-Making: The make_decision function now considers both current and predicted risk, enabling more informed actions. For instance, the car might choose to brake even if the current risk is moderate but the predicted risk is high due to an approaching obstacle.

Further Considerations for AI Development:

- Advanced Prediction Models: Implement more sophisticated prediction models using machine learning techniques like recurrent neural networks (RNNs) or long short-term memory (LSTM) networks to capture complex temporal patterns and dependencies in traffic behavior.

- Behavior Cloning: Train AI models on human driving data to learn and replicate safe driving behaviors, adapting to various scenarios and road conditions.

- Reinforcement Learning: Explore reinforcement learning techniques to train AI agents that can learn optimal driving strategies through trial and error in a simulated environment, leading to more adaptable and intelligent decision-making.

- Multiple Obstacles: The script now handles a list of obstacles, each represented by its distance and angle relative to the car's heading.

-Path Planning: The plan_path function implements a basic path planning strategy, prioritizing avoiding the closest obstacle by braking or steering away from its path.

- Dynamic Obstacles: Obstacles are now predicted to have relative speed and acceleration, making their behavior more realistic.

- Trajectory Planning: The script evaluates multiple potential steering angles and selects the one that minimizes the overall risk based on predicted obstacle trajectories and lane position.

- Cost Function: The cost_function evaluates the overall cost of a trajectory based on the combined risk of collisions with obstacles and the penalty for deviating from the lane center. This allows for a more nuanced evaluation of trajectory options.

- Optimization Approach: The script now samples multiple steering angles and selects the one that minimizes the cost function, leading to a more optimal trajectory selection.

Challenges and Considerations:

- Computational Complexity: Handling multiple obstacles and performing real-time path planning require efficient algorithms and sufficient computational resources.

- Sensor Limitations: Real-world sensors have limitations in range, accuracy, and update rates, which need to be considered in the decision-making process.

- Safety and Validation: Thorough testing and validation are crucial to ensure the safety and reliability of any path planning and obstacle avoidance system.

- Cost Function Design: Defining a comprehensive and effective cost function is crucial for achieving desired driving behavior and balancing different objectives.

- Optimization Efficiency: Optimization algorithms can be computationally expensive, requiring efficient implementations and potentially hardware acceleration for real-time applications.

- Uncertainty and Noise: Real-world sensor data and predictions contain uncertainties and noise, which need to be accounted for in the planning process.

Disclaimer:

This version showcases the integration of optimization techniques for trajectory planning, further refining the decision-making capabilities of the AI car. Continued research and development in these areas are critical for achieving safe and reliable autonomous driving systems.