Page 829 - AI for Good Innovate for Impact
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AI for Good Innovate for Impact
Use Case 9: Environment-Based Endurance Testing in Autonomous
Vehicles Transport 4.10: Intelligent
Country: India
Organization: Sri Sivasubramaniya Nadar College of Engineering
Contact Person(s): Hemamala V S - hema2210733@ ssn .edu .in
Kiruthick Raja D -kiruthickraja@ gmail .com
Kaythry P- kaythryp@ ssn .edu .in
1 Use Case Summary Table
Item Details
Category Intelligent Transport
Problem Range anxiety and suboptimal route planning in autonomous vehicles due
Addressed to static battery estimation, lack of terrain-awareness, and absence of real-
time intelligence
Key Aspects of • Real-time battery consumption prediction using LSTM / Transformer
Solution models.
• Context-aware driving assistance
• Smart charging station selection using RL.
Technology Terrain Analysis, Adaptive Routing, Battery Estimation, Deep Learning, Time
Keywords Series Forecasting, Reinforcement Learning, Real-time Navigation, Smart
Charging
Data Availability (Sample dataset for Initial Prototyping phase)
• EV battery consumption, Charging Patterns: [8]
• Elevation, Congestion, Road Condition: [9]
• Temperature, Humidity: [10]
• Energy Consumption Benchmark: [11]
Metadata Source (Type of Data): Metadata collected
Sources Vehicle Sensors (CAN): Speed, SoC, battery temperature, regenerative
and braking, power draw
Data collected GPS & Maps API: Coordinates, elevation, road gradient, curvature
Traffic APIs: Real-time congestion, average speed, and roadblocks
Weather APIs: Temperature, humidity, wind speed, and precipitation
Charging Stations: Availability, connector type, estimated queue, energy
pricing
Driving Behavior: Acceleration/deceleration patterns, idling time, and
frequent braking
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