Autonomous Vehicle Basis Paths for Lane Change Merge Planning
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently and safely navigating through complex environments, particularly in lane changes and obstacle avoidance, due to the lack of effective path planning systems that consider drivability constraints and real-time environmental factors.
Innovation Solution
A vehicle computing system generates basis paths for autonomous vehicles by identifying lane change regions, determining merge points, and evaluating candidate paths based on drivability constraints, using sensor data and map information to select optimal trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a path planning system generates multiple candidate basis paths to improve navigation safety and efficiency, then the vehicle's ability to react to events and obstacles is enhanced, but processing cycles, data storage, and energy consumption increase
Solution Approach 1:
The path planning is segmented into multiple independent candidate basis paths, each representing a different navigation strategy (e.g., conservative, aggressive, efficient). This segmentation allows the system to evaluate multiple options without processing every possible trajectory, reducing overall computational energy consumption while maintaining safety through diverse path candidates.
Solution Approach 2:
The system varies key parameters of the basis paths such as speed profiles, acceleration rates, and timing characteristics to generate diverse candidate paths. By changing these parameters systematically rather than generating completely different paths, the system reduces computational load while still providing multiple viable options for safe navigation.
2Adaptability or versatility
If the system generates multiple candidate basis paths with different timing characteristics, then the vehicle can better handle merge scenarios and lane changes, but processing complexity increases
Solution Approach 1:
The basis paths incorporate dynamic timing characteristics that adapt to real-time conditions. Each candidate path has variable speed profiles and timing parameters that can be adjusted based on detected events and obstacles, allowing the system to handle diverse scenarios like merges and lane changes without requiring completely separate path planning algorithms for each situation.
Solution Approach 2:
The candidate basis paths are designed to be multi-functional, serving multiple purposes such as merge preparation, lane changing, and obstacle avoidance within a single unified framework. This universality reduces processing complexity by avoiding the need for separate specialized algorithms for each maneuver type.
3Reliability
If the path planning system considers drivability constraints and real-time environmental factors, then navigation safety is improved, but data storage requirements increase
Solution Approach 1:
The system extracts and prioritizes only the most critical drivability constraints and environmental factors needed for basis path generation, rather than storing and processing all available sensor data. This selective extraction maintains safety by focusing on essential parameters while significantly reducing data storage requirements.
Solution Approach 2:
The system applies partial action by considering a subset of all possible constraints and factors - specifically those most relevant to the current navigation context. This approach provides sufficient safety for basis path planning without the excessive data storage burden of analyzing every possible environmental variable.
Data Source
AI summary
Systems and methods for basis path generation are provided. In particular, a computing system can obtain a target nominal path. The computing system can determine a current pose for an autonomous vehicle. The computing system can determine, based at least in part on the current pose of the autonomous vehicle and the target nominal path, a lane change region. The computing system can determine one or more merge points on the target nominal path. The computing system can, for each respective merge point in the one or more merge points, generate a candidate basis path from the current pose of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate basis path. The computing system can select one or more candidate basis paths based on the suitability classification for each respective candidate basis path in the plurality of candidate basis paths.