Autonomous Vehicle Speed Planning for Smooth Real-Time Transitions
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Solution Overview
Problem
Current speed behavior planning systems for autonomous vehicles fail to effectively smooth speed discontinuities and optimize speed behaviors in real-time, leading to potential safety issues and mechanical stress due to sudden changes in speed limits and environmental factors.
Innovation Solution
A method that receives current speed behavior factors, derives candidate speed behaviors by concatenating and smoothing speed limits along a trajectory, and uses optimization algorithms to determine definitive speed behaviors, considering past behaviors, mechanical limitations, and environmental conditions, while communicating with remote devices for self-driving capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speed limits are derived from multiple speed behavior factors (legal specifications, moving objects, events, mechanical limitations, weather conditions, road features), then the safety and reliability of autonomous driving is improved, but the complexity of the speed behavior planning system increases due to the need to aggregate and smooth multiple discontinuous speed limits
Solution Approach 1:
The speed behavior planning system segments the speed behavior factors into distinct categories (legal specifications, moving objects, events, mechanical limitations, weather conditions, road features) and processes each segment separately before aggregation. This segmentation allows the system to manage complexity by handling each factor type with appropriate processing rules while maintaining overall safety through comprehensive coverage of all speed-influencing elements.
Solution Approach 2:
The system performs preliminary actions by pre-defining speed behavior rules and aggregation functions for each type of speed behavior factor before runtime. Speed limits are pre-processed and stored as discrete segments, and the smoothing algorithms are pre-configured to handle discontinuities. This preliminary preparation reduces real-time computational complexity while ensuring safety through thorough pre-planning of speed behavior responses.
2Strength
If the system smooths speed discontinuities along the trajectory, then mechanical stress on the vehicle is reduced and driving comfort is improved, but the time and computational resources required to process speed behaviors increase
Solution Approach 1:
The system applies partial smoothing actions by selectively smoothing only the most critical speed discontinuities that would cause significant mechanical stress or safety issues, rather than smoothing all discontinuities uniformly. The smoothing intensity and scope are adjusted based on the severity of the discontinuity and the vehicle's current state, reducing unnecessary computational overhead while still protecting against harmful mechanical stress.
Solution Approach 2:
The system dynamically changes smoothing parameters (such as smoothing radius, intensity, and time constants) based on the specific characteristics of each speed discontinuity and the vehicle's operational context. By adjusting these parameters in real-time, the system achieves effective mechanical stress reduction while minimizing computational time through adaptive parameter optimization rather than fixed-parameter processing.
3Productivity
If the system uses optimization algorithms to derive definitive speed behavior from candidate speed behaviors, then the efficiency and smoothness of driving is improved, but the computational complexity and processing time increase
Solution Approach 1:
The optimization algorithm applies local quality optimization by focusing computational efforts on specific segments of the trajectory where speed behavior improvements are most needed, rather than uniformly optimizing the entire route. The system identifies critical zones (such as areas with multiple speed behavior factors or historical inefficiency points) and applies intensified optimization locally, reducing overall computational complexity while maintaining high driving efficiency in critical areas.
Solution Approach 2:
The system performs preliminary optimization actions by pre-calculating optimal speed behaviors for common trajectory patterns and speed behavior factor combinations. These pre-computed optimization results are stored and reused when similar conditions occur, significantly reducing real-time computational complexity while maintaining high driving efficiency through intelligent caching and pattern recognition.
4Stability of the object's composition
If the system considers past driven speed behaviors and definitive speed behaviors in real-time planning, then the smoothness and continuity of speed transitions is improved, but the memory requirements and processing overhead increase
Solution Approach 1:
The system extracts only the essential features and patterns from past driven speed behaviors and definitive speed behaviors, rather than storing and processing complete historical data. By extracting key characteristics (such as typical transition patterns, critical speed points, and common discontinuity types), the system achieves smooth speed transitions while minimizing memory requirements through feature-based rather than data-based historical analysis.
Solution Approach 2:
The system changes the representation parameters of historical speed behavior data by transforming detailed speed profiles into compressed parameter sets (such as key speed points, transition rates, and behavioral patterns). This parameter transformation reduces memory requirements while preserving the essential information needed for smooth speed transition planning through efficient data representation.
Data Source
AI summary
Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.