Autonomous Driving Scenario Parameters for Real-Time Traffic Adaptation
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Solution Overview
Problem
Autonomous driving vehicles (ADVs) face inefficiencies when using fixed scenario parameters for navigating different driving scenarios, as these do not account for varying environmental conditions and time-specific traffic complexities, leading to suboptimal navigation.
Innovation Solution
The system dynamically generates scenario parameters using a map-based scenario checker and neural network models that adjust based on real-time environmental data and vehicle status information, mimicking human-like driving behaviors for each scenario.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed scenario parameters are used for all driving scenarios, then the system is simple and easy to operate, but the navigation efficiency and adaptability to different environmental conditions deteriorate
Solution Approach 1:
The patent implements dynamic scenario parameters that automatically adjust based on real-time environmental conditions, vehicle status, and scenario characteristics. The system transitions from static fixed parameters to dynamic adaptive parameters that change according to traffic density, weather, time of day, and other contextual factors, thereby improving navigation efficiency while maintaining system manageability through automated adjustment.
Solution Approach 2:
The system changes parameters based on environmental conditions and scenario types. Different parameter sets are selected and adjusted according to the specific driving scenario (e.g., junction, turn, straight lane) and real-time conditions (e.g., traffic density, weather), allowing the vehicle to optimize its behavior for each situation rather than using a single fixed parameter set.
2Device complexity
If the same fixed parameters are used for all scenarios of the same type, then the device complexity is low, but the adaptability to different locations and times deteriorates
Solution Approach 1:
The patent applies local quality by tailoring parameter values to specific locations, times, and environmental conditions. Each driving scenario receives customized parameters based on its unique characteristics (e.g., a junction in a dense urban area during rush hour receives different parameters than the same junction during off-peak hours), rather than applying a uniform parameter set across all instances.
Solution Approach 2:
The system dynamically adjusts parameters based on real-time conditions, making the parameter set flexible and adaptive to changing environments. This allows the same scenario type to have different parameters at different times and locations, improving versatility without requiring a completely complex custom parameter system for every possible variation.
3Ease of manufacture
If fixed parameters are used regardless of traffic conditions, then the system is simple to implement, but the safety and efficiency in complex traffic situations deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor environmental conditions, traffic density, and vehicle status, then use this information to adjust scenario parameters in real-time. This feedback loop enables the system to respond to complex traffic situations dynamically, improving safety and reliability while maintaining implementation feasibility through structured feedback processing.
Data Source
AI summary
According to some embodiments, systems, methods and media for dynamically generating scenario parameters for an autonomous driving vehicles (ADV) are described. In one embodiment, when an ADV enters a driving scenario, the ADV can invoke a map-based scenario checker to determine the type of scenario, and invokes a corresponding neural network model to generate a set of parameters for the scenario based on real-time environmental conditions (e.g., traffics) and vehicle status information (e.g., speed). The set of scenario parameters can be a set of extra constraints for configuring the ADV to drive in a driving mode corresponding to the scenario.