###### Abstract  
The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers' behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers' behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.  
a 2021 2021 3 November 2021 7 November  
movement, and the standard deviation of lane changes and position [7]. Furthermore, physiological signal parameters, such as a proccardiograph (ECG), electroencephalogram (EEG), and electromyogram (EMG), are used to measure health issues [8]. Therefore, driver, vehicle, and roadside parameters are utilized to continuously monitor driver behavior [9; 10].  
Most of the previous works in this field lack in identifying the various driver and vehicle parameters [11]. Such driver behavior monitoring suffers by not alerting roadside pedestrians and other vehicles that are traveling on the non-safe driver's path [12]. Some of the input parameters for monitoring driver behavior are gravity, throttle, speed, revolutions per minute, and acceleration. The types of driver behavior are normal, aggressive, distracted, drowsy, and drunk driving [13; 14]. This information is measured by sensors that are placed on the vehicle. Each vehicle is connected to the roadside units (RSU) and each vehicle has microphones, frontal cameras, and road cameras (as shown in Figure 1).  
In driver behavior monitoring, vehicle motion prediction and lane change detection are often utilized since drivers frequently change lanes with high motion [15]. This causes any risks to avoid nearby vehicles and roadside pedestrians [16]. As a result, vehicle velocity, distance, and the position of the vehicle are observed to predict the sudden change of motion from past sequences [17]. There are two successive procedures that are implemented if any abnormal behaviors are detected: route planning and personalized assistance [18]. Route planning based on drivers' current preferences was not discussed earlier. This is significant in monitoring and providing assistance to drivers to ensure traffic and driver safety [19]. Hence, both assistance and route planning is provided in this study. Moreover, minimal research has been presented regarding disseminating warning messages to nearby vehicles [20].