Autonomous Vehicle Velocity Planning for Multi-Directional Collision Avoidance
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Solution Overview
Problem
Conventional intelligent vehicle anti-collision systems rely solely on distance and minimum braking time, leading to potential mis-determination or missed determination of collision risks, which can result in injuries or vehicle damage. Additionally, these systems are inadequate in complex scenarios where collisions can occur from multiple directions or when vehicles travel against the direction of traffic.
Innovation Solution
The proposed method involves determining an optimal velocity for an intelligent vehicle by considering the first and second planned velocities, which include direction and magnitude, and the risk of collision with surrounding obstacles. This is achieved through the use of redundancy in dual channels for planning velocities and assessing collision potential energy, allowing the vehicle to avoid obstacles effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anti-collision systems use only distance and minimum braking time for collision determination, then the system complexity is low, but the reliability of collision detection deteriorates due to mis-determination or missed determination
Solution Approach 1:
The collision detection system is segmented into multiple independent channels (first channel and second channel), each performing collision determination using different parameters. The first channel uses distance and minimum braking time, while the second channel uses relative velocity and safe velocity. This segmentation allows each channel to specialize in specific detection aspects, improving overall reliability without requiring a single complex system to handle all detection tasks.
Solution Approach 2:
The patent implements a redundant detection mechanism where a second channel copies the essential function of collision detection but uses different parameters (relative velocity and safe velocity) compared to the first channel. This copying approach with parameter variation ensures that if one channel fails or gives false results, the other channel can provide accurate collision detection, thereby improving reliability without significantly increasing system complexity.
2Adaptability or versatility
If the system only performs braking operation to avoid collision, then the control mechanism is simple, but the adaptability to complex scenarios deteriorates when collisions may occur from multiple directions or against traffic flow
Solution Approach 1:
The control mechanism transitions from a static braking-only approach to a dynamic multi-directional control system. The system now determines optimal velocity vectors that can include braking, acceleration, or directional changes based on the relative position and velocity of obstacles. This dynamic approach allows the vehicle to adapt to complex scenarios such as obstacles from multiple directions or against traffic flow, improving scenario adaptability while maintaining reasonable control mechanism complexity through systematic velocity planning.
Solution Approach 2:
The system changes the control parameters from simple braking force to comprehensive velocity vectors including magnitude and direction. By calculating optimal velocity based on relative velocity between vehicle and obstacle, along with safe velocity thresholds, the system can generate appropriate control commands (braking, acceleration, steering) adapted to different scenario complexities, thereby improving versatility without excessive mechanism complexity.
3Measurement precision
If conventional systems determine collision risk only based on distance and braking time, then the measurement process is simple, but the measurement precision deteriorates leading to mis-determination or missed determination
Solution Approach 1:
The detection system adds another dimension to collision risk assessment by introducing relative velocity as an additional measurement parameter alongside distance and braking time. The second channel specifically measures relative velocity between the vehicle and obstacles, providing a new dimensional perspective on collision risk. This multi-dimensional measurement approach significantly improves measurement precision by capturing dynamics that distance-only measurements miss, while the structured addition of parameters keeps detection complexity manageable.
Data Source
AI summary
Disclosed is a vehicle control method, including: obtaining a first velocity planned for an intelligent vehicle to travel in a first area; and obtaining a second velocity planned for the vehicle to travel in the first area, where the second velocity is obtained based on a collision potential energy, the first velocity and the second velocity each include a direction and a magnitude, and the first velocity, the second velocity, and a risk of a collision between the vehicle and a surrounding obstacle are used to determine an optimal velocity of the vehicle, so that the vehicle can effectively avoid the obstacle, thereby improving traveling safety of the vehicle. Also disclosed are a vehicle control apparatus, a vehicle controller, and a vehicle.