Driver evaluation in heavy duty vehicles based on acceleration and braking behaviors

In this paper, we present a real-time driver evalua-tion system for heavy-duty vehicles by focusing on the classifica-tion of risky acceleration and braking behaviors. We utilize animproved version of our previous Long Short Memory (LSTM)based acceleration behavior model [10] to evaluate varyingacceleration behaviors of a truck driver in small time periods.This model continuously classifies a driver as one of six driverclasses with specified longitudinal-lateral aggression levels, usingdriving signals as time-series inputs. The driver gets accelerationscore updates based on assigned classes and the geometry ofdriven road sections. To evaluate the braking behaviors of atruck driver, we propose a braking behavior model, which usesa novel approach to analyze deceleration patterns formed duringbrake operations. The braking score of a driver is updated foreach brake event based on the pattern, magnitude, and frequencyevaluations. The proposed driver evaluation system has achievedsignificant results in both the classification and evaluation ofacceleration and braking behaviors

Positioning Eye Fixation and Vehicle Movement: Visual-motor Coordination Assessment in Naturalistic Driving

In recent years, many driving studies in the traffic safety literature have undertaken error assessments of driver behaviour. However, few studies have been able to analyse the detailed individual vision and motor behaviours of drivers, due to the lack of reliable data and available technologies. Therefore, little is currently known about drivers' visual-motor coordination involving the use of visual information to regulate their physical movements. This research sets-up a technical framework to investigate on-road drivers' visual-motor coordination via vision tracking and vehicle positioning. The driving behaviour and performance were recorded and analysed using Eye Movement Tracking, Global Navigation Satellite System (GNSS) and Geographic Information Systems (GIS). The eye tracker recorded eye fixations and duration on video images to analyse the visual pattern of individual drivers. Real-time kinematic (RTK) post-processing of multi-GNSS generated vehicle movement trajectory at centimetre-level accuracy horizontally, which encompasses precise lateral positioning, speed and acceleration parameters of driving behaviours. The eye fixation data was then geocoded and synchronised with the vehicle movement trajectory in order to investigate the visual-motor coordination of the drivers. A prototype of implementation of the framework focusing on complex U-turn manoeuvre at a roundabout in five older drivers was presented in this paper. The visualisation of spatial-temporal patterns of visual-motor coordination for individual drivers allows for a greater insight to behaviour assessment. The on-road driving test in this study has also demonstrated a discriminant and ecologically valid approach in driving behaviour assessment, which can be used in studies with other cohort population

Analyzing Crash Potential in Mixed Traffic with Autonomous and Human-Driven Vehicles

Reducing crash counts on saturated road networks is one of the most significant benefits behind the introduction of Autonomous Vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to the car-following scenarios between AVs and human drivers. A mismatch in the braking and accelerating decisions in this car-following scenario can lead to rear-end near-crashes and therefore needs to be studied. This thesis aims to investigate the driving behavior of human-drivers that follow a designated AV leader in a car-following situation and compare the results with a scenario when the leader is a human-like driver. In this study, speed trajectory data was collected from 48 participants using a driving simulator. To estimate the near-crash risk between the participants and the leading vehicle, critical thresholds of six Surrogate Safety Measures (SSMs): Time to Collision (TTC), Inverse Time to Collision (ITTC), Modified Time to Collision (MTTC), Deceleration Rate to Avoid Crash (DRAC), critical jerk and Warning Index (WI), were used. The potential near-crash events and the safe driving events were classified using a random forest algorithm after performing oversampling and undersampling techniques. The results from the two-sample t-tests indicated a significant difference between the overall deceleration rates, braking speeds, and acceleration rates of the participants and the designated AV leader. However, no such difference was found between the participants and the human-like leader while braking and accelerating at stop-controlled intersections. Out of six SSMs, MTTC detected near-crash events 10 seconds before their actual occurrence at a range of 11.93 m with 83% accuracy. The surrogate measures identified a higher number of near-crash (high risk) events when the participants followed the designated AV and made braking maneuvers at the stop-controlled intersections. Based on the number of near-crash (high risk) events, the designated AV's C3.25 speed profile (with the maximum deceleration rate of 3.25 m/s2 ) posed the highest crash risk to the participants in the following vehicle. For potential near-crash events classification, a random forest classifier based on undersampled data achieved the highest average accuracy rate of 92.2%. The deceleration rates of the designated AV had the highest impact on the near-crashes between the AV and the participants. However, shorter clearances during the braking maneuvers at intersections significantly affected the near-crashes between the human-like leader and the participants in the following vehicle

Quantum Artificial Intelligence Supported Autonomous Truck Platooning

Truck platooning can potentially increase the operational efficiency of freight movement on U.S. corridors, improving commercial productivity and economic vibrancy. Predicting each leader vehicle trajectory in the autonomous truck platoon using Artificial Intelligence (AI) can enhance platoon efficiency and safety. Reliance on classical AI may not be efficient for this purpose as it will increase the computational burden for each truck in the platoon. However, Quantum Artificial Intelligence (AI) can be used in this scenario to enhance learning efficiency, learning capacity, and run-time improvements. This study developed and evaluated a Long Short-Term Memory Networks (LSTM) model and a hybrid quantum-classical LSTM (QLSTM) for predicting the trajectory of each leader vehicle of an autonomous truck platoon. Both the LSTM and QLSTM provided comparable results. However, Quantum-AI is more efficient in real-time management for an automated truck platoon as it requires less computational burden. The QLSTM training required less data compared to LSTM. Moreover, QLSTM also used fewer parameters compared to classical LSTM. This study also evaluated an autonomous truck platoon\u27s operational efficacy and string stability with the prediction of trajectory from both classical LSTM and QLSTM using the Intelligent Driver Model (IDM). The platoon operating with LSTM and QLSTM trajectory prediction showed comparable operational efficiency. Moreover, the platoon operating with QLSTM trajectory prediction provided better string stability compared to LSTM