Unveiling pre-crash driving behavior common features based upon behavior entropy

One-pedal or two-pedal: Does the regenerative braking system improve driving safety?

Electric vehicles equipped with regenerative braking systems provide drivers a new driving mode, the one-pedal mode, which enables drivers to accelerate and decelerate with the throttle alone. However, there is a lack of systematic research on driving behavior in one-pedal mode, and whether it actually enhances or reduces safety remains to be validated. A driving simulator was used to analyze driving behavior and safety in the one-pedal mode in situations with different urgency level, with the two-pedal mode (the traditional driving mode in internal combustion engine vehicles) serving as a comparative group. The driver's perception times, initial and final throttle release times, throttle to brake transition times, maximum brake pedal forces, collision ratios, and time-to-collision (TTC) were measured under the lead vehicle decelerating at 0.1 g, 0.2 g, 0.5 g, 0.75 g, as well as uncertainty (decelerating at 0.2 g to 25 km/h, then decelerating at 0.75 g to 0), and under headways of 1.5 s and 2.5 s. Results showed: 1) The regenerative braking system did not affect driver perception and reaction of the lead vehicle braking event and drivers extended throttle release to avoid rapid speed drops when the lead vehicle braked slowly; 2) the one-pedal mode exhibited a longer throttle to brake transition time and increased uncertainty in timing of brake pedal application; 3) the one-pedal mode was safer than the two-pedal mode in low urgency situations but became unsafe in high urgency or uncertain situations due to delayed braking. The implications of this research include enhancing regenerative braking systems and developing forward collision warning systems.

High-risk event prone driver identification considering driving behavior temporal covariate shift

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.