Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated...

Area of Science:

• Human-computer interaction
• Automotive engineering
• Cognitive psychology

Background:

• Level 2 automated driving systems (ADS) require driver intervention in complex scenarios.
• Human-machine interfaces (HMIs) are crucial for effective driver engagement and safety in ADS.
• Understanding HMI impact on driver behavior is vital for safe ADS deployment.

Purpose of the Study:

• To evaluate the impact of static and sensor HMIs on driver interventions during level 2 automated driving.
• To assess the effectiveness of HMIs in preventing rear-end collisions caused by sudden cut-ins.
• To investigate the influence of HMIs on driver attention and safety perception.

Main Methods:

• A driving simulator study with 20 participants was conducted.
• Two HMIs were tested: static (intersection warnings) and sensor (object recognition).
• Experimental conditions included manual driving, L2 ADS without HMI, and L2 ADS with combined HMIs.

Main Results:

• Combined static and sensor HMIs enabled comparable collision avoidance with significantly less braking deceleration.
• L2 ADS without HMI resulted in greater deceleration than manual driving.
• Driver attention to the road and perceived safety were significantly higher with combined HMIs.

Conclusions:

• The combination of static and sensor HMIs enhances driving safety in level 2 automated driving.
• Integrated HMIs improve driver situational awareness and reduce the need for harsh braking.
• This HMI strategy supports safer application of automated driving in complex traffic.