Multi-Lane Driving Support Strategy for Obstructed Ego Lanes
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Solution Overview
Problem
Existing Advanced Driver Assistance Systems (ADAS) struggle to handle complex multi-lane traffic scenarios where the ego lane is partly or fully obstructed, leading to potential collisions due to unpredictable events and unclear situations, overtaxing level 2 or level 3 automation systems.
Innovation Solution
A method and system that utilize an environment sensor system to measure traffic surroundings, including data on traffic and free space, and a decision device to evaluate these surroundings using a cost function to choose from six strategies to avoid collisions, prioritizing collision avoidance and minimizing driver discomfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If level 2 or level 3 automation systems are used to handle complex multi-lane traffic scenarios, then the system can provide some level of automated driving support, but the systems become overtaxed and fail to handle unpredictable events and unclear situations effectively
Solution Approach 1:
The patent introduces a multi-strategy decision device that acts as an intermediary between the sensor system and the execution systems. This decision device evaluates multiple strategies (braking, steering, lane changing) and selects the most appropriate one based on the current traffic situation, thereby mediating the complex information processing requirements and improving system reliability in unpredictable scenarios.
Solution Approach 2:
The system dynamically adapts its behavior by selecting from multiple strategies based on real-time traffic conditions. The decision device continuously evaluates the current situation and switches between different driving strategies (braking, steering, lane changing) as needed, making the automation system more flexible and reliable in handling unpredictable events.
2Reliability
If the system implements multiple strategies for collision avoidance, then collision avoidance capability is improved, but the decision-making complexity increases
Solution Approach 1:
The decision-making process is segmented into distinct strategies (braking, steering, lane changing), each handling specific types of collision avoidance scenarios. The decision device selects and executes one strategy at a time based on the current situation, which simplifies the overall complexity while maintaining comprehensive collision avoidance capability.
Solution Approach 2:
The system changes its operational parameters by switching between different driving strategies based on the evaluated situation. The decision device adjusts the driving behavior parameters (braking force, steering angle, lane change timing) according to the selected strategy, enabling effective collision avoidance without requiring simultaneously complex decision-making for all parameters.
3Reliability
If the system provides comprehensive driving support in complex scenarios, then driver safety and confidence are enhanced, but the system requires higher computational resources and processing power
Solution Approach 1:
The system implements partial automation by providing driving support specifically for complex multi-lane scenarios rather than full automation. The decision device activates comprehensive analysis and multiple strategies only when needed (in obstructed ego lane situations), reducing computational energy consumption during normal driving while maintaining high driver safety in complex scenarios.
Data Source
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Figure 3
AI summary
The present invention refers to a method for providing a multi-lane scenario driving support for an ego vehicle (10) in a traffic situation. Traffic surroundings are measured by an environment sensor system (14), whereby the traffic surroundings include data about traffic and free space within an ego lane (16) of the ego vehicle (10) and at least an adjacent lane (12a, 12b), and data about front proximity area (18) and rear proximity area (20) of the ego vehicle (10). A decision device (22) evaluates the measured traffic surroundings and decides a driving operation to be executed by the ego vehicle (10) based on at least one strategy. In the decision device (22) a cost function is used for choosing one of at least six strategies, the cost function being based on at least a core priority, whereby the core priority is to avoid collision of the ego vehicle (10) and not cause collision of the ego vehicle (10) with a third party vehicle (24). The decision device (22) by means of the cost function chooses one of at least the following six strategies: braking in the ego lane (16), to combine braking and steering within the ego lane (16) of the ego vehicle (10), steering within the ego lane (16) of the ego vehicle (10) to avoid an obstacle, to full-brake in the ego lane (16) of the ego vehicle (10), to combine braking and steering towards or when entering temporarily an adjacent lane (12a, 12b) and steering towards or when entering temporarily an adjacent lane (12a, 12b).