Automated Vehicle Training System for Anomalous Event Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current driver training methods for automated driving systems (ADS) are inefficient and inconvenient, particularly in simulating anomalous driving events such as edge or corner scenarios, making it difficult to effectively train drivers on the limitations and capabilities of ADS vehicles.
Innovation Solution
A training system that determines safe conditions to simulate anomalous driving events while driving, using processors and memory to assess vehicle behavior and control the vehicle's actions, such as braking or disengaging ADS, to teach drivers about ADS limitations and capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver training is conducted through traditional methods (vehicle manual, driving school, virtual training system), then training can be provided without actual driving, but the training is time-consuming, inconvenient, or ineffective at comprehensively training a driver
Solution Approach 1:
The system performs preliminary safety assessment and determination before actual training simulation. The determination module evaluates whether simulating the selected anomalous driving event will affect safety of entities, and only proceeds with training simulation when safety conditions are satisfied, thus preparing training conditions in advance to ensure both effectiveness and time efficiency
Solution Approach 2:
The system creates a controlled copy of anomalous driving events (edge scenarios, corner scenarios, faulty system events) that can be safely simulated during actual driving. Instead of using virtual training systems that may not fully replicate real conditions, the system copies real anomalous events into a controllable simulation mode during actual driving, providing comprehensive training without excessive time consumption
2Reliability
If the system simulates anomalous driving events during actual driving to provide comprehensive training, then driver training effectiveness improves, but safety risks may increase if not properly controlled
Solution Approach 1:
The system applies preliminary anti-action by performing safety determination before simulating anomalous driving events. The determination module assesses whether the simulation will affect safety of entities (pedestrians, other vehicles, occupants), and only allows simulation when safety conditions are satisfied, thus preventing harmful effects before they can occur
Solution Approach 2:
The determination module acts as an intermediary between the training simulation function and the actual driving environment. It mediates by evaluating safety conditions and controlling whether the simulation proceeds, ensuring that training effectiveness is achieved without compromising safety of entities in the real driving environment
3Loss of information
If the system provides comprehensive training on all anomalous driving events, then driver understanding of ADS limitations improves, but the complexity of the training system increases
Solution Approach 1:
The system applies local quality by allowing selective simulation of specific anomalous driving events based on driver selection and current driving conditions. Instead of forcing comprehensive training on all possible anomalies simultaneously, the system enables targeted training on specific events (edge scenarios, corner scenarios, faulty system events) that are relevant to the current context and driver needs, reducing overall system complexity while maintaining comprehensive training capability
4Adaptability or versatility
If the system allows driver selection of specific anomalous driving events for training, then training adaptability improves, but the time required for event selection and system configuration increases
Solution Approach 1:
The system enables self-service by allowing the driver to directly select the type of anomalous driving event they wish to train on, without requiring extensive system configuration or intermediary setup. The driver interface allows direct selection from available anomaly types, and the system automatically configures and executes the training simulation, thus providing adaptability while minimizing configuration time
Data Source
AI summary
System, methods, and other embodiments described herein relate to a training system to train a driver about occurrences of anomalous driving events of automated vehicle systems. In one embodiment, a method includes determining, upon receiving a selection of a vehicle behavior from one or more anomalous driving events and a detected state change signal, whether the vehicle behavior affects one or more entities. The method includes assessing a state of the one or more entities to simulate the vehicle behavior according to a safety standard. The method includes triggering simulation of the vehicle behavior if the state satisfies a threshold. The method includes simulating the vehicle behavior by at least controlling the vehicle to simulate the vehicle behavior during automated driving of the vehicle.