Collision Reduction Research Articles

Change in the collision avoidance performance of autonomous emergency braking systems

Objective This study quantifies the change in collision avoidance performance of autonomous emergency braking (AEB) systems for traffic accidents in Japan since 2015. Method This study used data on Japanese traffic accidents compiled by Japan’s National Police Agency. The data included only accidents involving loss of or injury to human life; accidents involving only property damage were excluded. We restricted our analysis to collisions between two 4-wheel vehicles and considered only collisions for which we could determine whether the primary party’s car was equipped with an AEB system. Both Poisson and negative binomial mixed-effects regression analyses were conducted using the data for 2021 and 2022 to measure the collision avoidance performance of first registered cars in 2015 to 2020 equipped with AEB systems compared with cars without AEB systems first registered in 2015. Collision avoidance performance was measured for 2 types of intervehicle collisions: rear-end collisions and right-turn collisions. Collision avoidance performance for rear-end collisions was also measured for each of the 3 car types—Standard, small, and light cars. Results The collision reduction rate for rear-end collisions increases with the year of first registration and for cars equipped with AEB systems first registered in 2020 compared with non-AEB-equipped cars first registered in 2015 is 69.2% (95% confidence interval [CI] 67.0%–71.1%), indicating that the performance of AEB systems has dramatically improved in terms of preventing rear-end collisions. For right-turn collisions, the rate increased to 20.4% (95% CI 5.9%–32.6%) for cars equipped with AEB systems first registered in 2019. However, no clear trend is observed. Conclusions This study evaluated a time series of the collision reduction performance of AEB systems using an original methodology. Japan’s New Car Assessment Program (JNCAP) has included AEB’s effectiveness in reducing damage from traffic collisions as an evaluation item since FY2014. The results could demonstrate the effectiveness of JNCAP.

Enhancing end-to-end control in autonomous driving through kinematic-infused and visual memory imitation learning

This paper presents an exploration, study, and comparison of various alternatives to enhance the capabilities of an end-to-end control system for autonomous driving based on imitation learning by adding visual memory and kinematic input data to the deep learning architectures that govern the vehicle. The experimental comparison relies on fundamental error metrics (MAE, MSE) during the offline assessment, supplemented by several external complementary fine-grain metrics based on the behavior of the ego vehicle at several urban test scenarios in the CARLA reference simulator in the online evaluation. Our study focuses on a lane-following application using different urban scenario layouts and visual bird-eye-view input. The memory addition involves architectural modifications and different sensory input types. The kinematic data integration is managed with a modified input. The experiments encompass both typical driving scenarios and extreme never-seen conditions. Additionally, we conduct an ablation study examining various memory lengths and densities. We prove experimentally that incorporating visual memory capabilities and kinematic input data makes the driving system more robust and able to handle a wider range of challenging situations, including those not encountered during training, in terms of reduction of collisions and speed self-regulation, resulting in a 75% enhancement. All the work we present here, including model architectures, trained model weights, comparison tool, and the dataset, is open-source, facilitating replication and extension of our findings.

On the evolution of chromosomal regions with high gene strand bias in bacteria.

On circular bacterial chromosomes, the majority of genes are coded on the leading strand. This gene strand bias (GSB) ranges from up to 85% in some Bacillota to a little more than 50% in other phyla. The factors determining the extent of the strand bias remain to be found. Here, we report that species in the phylum Gemmatimonadota share a unique chromosome architecture, distinct from neighboring phyla: in a conserved 600-kb region around the terminus of replication, almost all genes were located on the leading strands, while on the remaining part of the chromosome, the strand preference was more balanced. The high strand bias (HSB) region harbors the rRNA clusters, core, and highly expressed genes. Selective pressure for reduction of collisions with DNA replication to minimize detrimental mutations can explain the conservation of essential genes in this region. Repetitive and mobile elements are underrepresented, suggesting reduced recombination frequency by structural isolation from other parts of the chromosome. We propose that the HSB region forms a distinct chromosomal domain. Gemmatimonadota chromosomes evolved mainly by expansion through horizontal gene transfer and duplications outside of the ancient high strand bias region. In support of our hypothesis, we could further identify two Spiroplasma strains on a similar evolutionary path.IMPORTANCEOn bacterial chromosomes, a preferred location of genes on the leading strand has evolved to reduce conflicts between replication and transcription. Despite a vast body of research, the question why bacteria show large differences in their gene strand bias is still not solved. The discovery of "hybrid" chromosomes in different phyla, including Gemmatimonadota, in which a conserved high strand bias is found exclusively in a region at ter, points toward a role of nucleoid structure, additional to replication, in the evolution of strand preferences. A fine-grained structural analysis of the ever-increasing number of available bacterial genomes could help to better understand the forces that shape the sequential and spatial organization of the cell's information content.

Effect of 20 mph speed limits on traffic injuries in Edinburgh, UK: a natural experiment and modelling study

IntroductionThere is limited research evaluating 20 mph speed limit interventions, and long-term assessments are seldom conducted either globally or within the UK. This study evaluated the impact of the phased...

Computational fluid dynamics simulation of passive downstream fish passage over a weir with OpenFOAM®

Many fish species are dependent on migration through river systems. Weirs are an obstacle to downstream migration, which can lead to obstruction as well as injury and mortality. The current level of knowledge about downstream migration over weirs is limited, and the current assessment of this is uncertain. This study uses a computational fluid dynamics (CFD) method for assessing the damage potential of the downstream migration over the weir, evaluating the effectiveness of the current recommendations. Two test cases were simulated using OpenFOAM® and an interFoam-based solver, extended with Lagrangian particles to represent passively transported fish. The results of the simulations show that adherence to the recommendation for a necessary tailwater level has no significant impact on the relevant hydraulic stressors, shear stress, pressure changes and collisions in certain situations. A thick nappe, however, leads to a significant reduction in collisions, especially of particles initialized near the surface. The CFD method used in this study proves to be suitable for evaluating downstream fish passage with certain limitations. This means that qualitative comparisons of different situations are possible, but not the determination of the damage potential of a specific situation. Further investigations are necessary to identify factors for safe downstream fish passage.

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks.

Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs' physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications.

Optimizing Vehicle-to-Vehicle Communication for Enhanced Automotive Safety and Collision Reduction

This manuscript presents a novel hybrid approach aimed at enhancing Vehicle-to-Vehicle (V2V) communication to bolsterroad safety and diminish collisions. The primary objective is to augment V2V communication to foster safer automobiles and diminish collision occurrences by developing and implementing a system capable of actively detecting, evaluating, and transmitting collision risks in real time. Given the escalating number of vehicles on roads, reliable communication among vehicles is imperative to ensure safety and avert collisions. The proposed system leverages the optimization of Arduino UNO controller parameters, employedto anticipate, and fine-tune the controller settings. Theoutcomes underscore the efficacy of the approach in fortifyingV2V communication for safer automobiles and fewer collisions. The experimental findings showcase a notable reduction in errors compared to alternative methodologies.

Decision analysis with IDOCRIW-QUALIFLEX approach in the 2TLq-ROF environment: An application of accident prediction models in Pakistan

In Pakistan, the assessment of road safety measures within road safety management systems is commonly seen as the most deficient part. Accident prediction models are essential for road authorities, road designers, and road safety specialists. These models facilitate the examination of safety concerns, the identification of safety improvements, and the projection of the potential impact of these modifications in terms of collision reduction. In the context described above, the goal of this paper is to utilize the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), a new and useful decision tool with a strong ability to address uncertain or imprecise information in practical decision-making processes. In addition, for dealing with the multi-attribute group decision-making problems in road safety management, this paper proposes a new 2TLq-ROF integrated determination of objective criteria weights (IDOCRIW)-the qualitative flexible multiple criteria (QUALIFLEX) decision analysis method with a weighted power average (WPA) operator based on the 2TLq-ROF numbers. The IDOCRIW method is used to calculate the weight of attributes and the QUALIFLEX method is used to rank the options. To show the viability and superiority of the proposed approach, we also perform a case study on the evaluation of accident prediction models in road safety management. Finally, the results of the experiments and comparisons with existing methods are used to explain the benefits and superiority of the suggested approach. The findings of this study show that the proposed approach is more practical and compatible with other existing approaches.

Adoption of ride share services associated with decreased organ donor availability

IntroductionRecent single center studies have shown an association between the introduction and adoption of ride sharing services and a significant reduction in alcohol-related motor vehicle collisions and fatalities at urban trauma centers. There is a lack of studies that have evaluated the effect of the introduction and widespread adoption of ride sharing services on the number of organ donors available from motor vehicle accidents (MVAs) and the effect on the overall donor pool. We hypothesize that the number of deceased donors from MVAs has decreased with the introduction of ride sharing. MethodsThe United Network for Organ Sharing (UNOs) database was retrospectively reviewed from September 29,1987 to June 30, 2022 for deceased donors. Only pediatric donors were excluded (age<15). Donors were analyzed by circumstance of death, which included: MVA, suicide, homicide, non-MVA accidents, natural causes, and other. The launch of the major ride sharing service in the United States occurred on July 15, 2010. This service was available in 100 cities in April 2014 and in 500 cities in December 2016. Data were analyzed in a continuous manner and then 4 major adoption periods were considered: pre-launch, launch to narrow adoption, narrow to wide adoption, and post wide adoption. Results218,606 deceased donors had the circumstance of death data available and were included in our analysis. The mean deceased donor age was 42 years, 60% of donors were male, and 71% were white. MVA proportion of death decreased 5% (p<0.001) from the launch to narrow adoption period (16%) compared to pre-launch (21%). Another 2% (p<0.001) decrease occurred during the narrow to wide adoption period (14%). An additional 2% (p<0.001) drop occurred after wide adoption (12%). Figure 1 shows stacked percentages of the various circumstances of death in a continuous manner. Of note: homicides, suicides, and non-MVA accidents were not consistently recorded until 1994, and natural causes until 2001. This graph demonstrates a notable drop in MVA percentages beginning after the launch of ride sharing with steady declines continuing to the period of wide adoption. While absolute numbers of MVA donors have steadily increased since 2014 and had steady numbers before, the other deceased donor categories have increased at a higher rate. ConclusionThe invention and adoption of ride sharing services has decreased the proportion of organ donors from MVAs. The overall number of donors from MVAs, donors from all other causes, and the total number of deceased donors increases each year.

Prioritization and scheduling in the vehicle-to-infrastructure (V2I) scheme for vanets using enhanced congestion control source based ant colony optimization

Mobile ad hoc networks (MANETs), which include vehicular ad hoc networks (VANETs), are a promising method for intelligent transportation systems. The major challenges of VANET protocols are symmetric links and changing topology of the networks and hence a proper routing method is necessary for VANET. A crucial issue with VANETs is the transmission of data from a starting point to the base station. To discover the least path with the lowest cost and fewest hops between the source and the destination, existing work uses a novel priority-based direction-aware collision avoidance (P-DVCA). To direct the ants to develop a shorter path with fewer hops for data transmission, the suggested method uses the prioritising and scheduling enhanced congestion control-based source ant colony optimization (ECC-ACO) for VANETs. Based on the message type and network situation, this technique provides precedence for privacy messages communications. Privacy messages can also be divided into beacon messages and occurrence messages. At that point, each message is routed into the appropriate queue predicated on its primary consideration, and each message in each queue is planned. When compared to renowned VANET communication architecture, simulation and analytical findings show that proposed ECC-ACO offers a respectable reduction in collisions.

How COVID-19 impacted the temporal and spatial distribution of collision hotspots

This research examines the spatial and temporal shift in collision hotspots caused by the COVID-19 pandemic, considering different collision severities. The Getis-Ord statistic was utilized to create spatial models and generate map outputs for 2019 and 2020. Two distinct approaches were employed: using a census tract shapefile (provided) and creating fishnet polygons measuring 500 m by 500 m. Results showed fewer hotspots outside Edmonton's central core, while fatal collisions were concentrated close to the core. This intriguing finding suggests that COVID-19 restrictions led to more aggressive driving behaviour near the centre, contributing to a rise in fatal collision numbers. The study found a significant reduction in traffic collisions in April 2020, with a 58% decrease compared to the previous year. The research highlights the pandemic's impact on road safety, emphasizing the importance of reducing traffic volume and advocating for traffic restrictions and control strategies, multi-modal planning, and efficient pricing strategies within Vision Zero for improved road safety.

Decentralised Motion Planning for Autonomous Mining Haulage Trucks using Prioritised Multi-Agent MPPI

This paper introduces a decentralised motion planning method for Autonomous Haulage Systems (AHS) using Prioritised Multi-Agent Model Predictive Path Integral Control (P-MA-MPPI). Our approach incorporates vehicle prioritisation, addressing a challenge that has previously limited decentralised MPPI frameworks, to enhance navigation safety and efficiency by exchanging future trajectories among agents. The experimental findings indicate a reduction in collision and deadlock rates when compared to an existing MPPI-based approach, alongside about a 20% increase in energy efficiency in complicated scenarios where three vehicles simultaneously enter an intersection. This research contributes to scalable and sustainable operation strategies, improving the efficiency and the environmental impact of future mine automation.

An Efficient and Scalable RFID Anti-Collision Algorithm on Optimal Partition and Collided Block Bit-Mapping

In RFID systems, many anti-collision algorithms, driven by the concept of rescheduling the response sequence between the reader and unidentified tags, have been put forward to solve tag collision problem, including ALOHA-based, tree-based and hybrid algorithms. In this paper, we propose a novel RFID anti-collision algorithm called EAQ-CBB, which adopts three main approaches: tag population estimation based on collided bit detection method, optimal partitions and trimmed query tree based on the strategy of collided block bit-mapping (QTCBB). The relatively accurate estimation of tag backlog and optimal partition ensure a great reduction of collisions in the initial phase. For each collided partition, a QTCBB process is introduced immediately, which eliminates all the empty slots and significantly reduces the collided slots. Simulation results show that EAQ-CBB performs good stability and scalability when the key parameters change. Compared with the existing algorithms, such as DFSA, QTI, T-GDFSA and CT, EAQ-CBB outperforms the others with high system throughput, low normalized latency and low normalized overhead at a low cost of energy, which makes it easier to be used widely in the efficient-aware and energy-aware applications.

RETRO-mapping: A novel algorithm automating wavefront categorization using activation mapping during persistent atrial fibrillation demonstrates a reduction in wavefront collisions following pulmonary vein isolation.

RETRO-mapping was developed to automate activation mapping of atrial fibrillation (AF). We used the algorithm to study the effect of pulmonary vein isolation (PVI) on the frequency of focal, planar, and colliding wavefronts in persistent AF. An AFocusII catheter was placed on the left atrial endocardium to record 3 s of AF at sixsites pre and post-PVI in patients undergoing wide circumferential PVI for persistent AF. RETRO-mapping analyzed each segment in 2 ms time windows for evidence of focal, planar, and colliding waveforms and the automated categorizations manually validated. Ten patients were recruited. A total of 360 s of data in 120 segments of 3 s from 60 left atrial locations were analyzed. RETRO-map was highly effective at identifying focal waves and collisions during AF. PVI significantly reduced collision frequency but not focal and planar activation frequency. However, there was a significant reduction in the dispersion of activation directions. Larger studies may help determine factors associated with successful clinical outcome.

The probability of escape of the products of charged particles resulting from thermonuclear fusion of advanced fuels in the hotspot

Establishing fuel ignition conditions depends on the stability of hotspot ignition. Excessive energy escape from the plasma leads to its cooling and shutdown. In this research, the escape probability of charged particles due to the p/11B fuel fusion has been investigated. First, the contribution of p/11B fuel plasma electrons in the Alpha particle energy loss based on the Krokhin and Rozanov (KR) model and then the auxiliary contribution of plasma ions in the Alpha particle energy loss produced in the hotspot of p/11B fuel, based on Li–Petrasso (LP) calculations were analyzed numerically. By calculating the escape fraction of Alpha particles, it has been shown that the contribution of the stopping power of the electrons of the plasma is dominant at both 70 and 170 keV temperatures, which are the starting temperature of the p/11B fuel reaction and the proper ignition temperature, respectively. By increasing the density of p/11B fuel from 300 to 400 g/cc, it can be seen that at a temperature of 70 keV, the penetration depth of Alpha particles decreases from 1200 to 900 μm based on the KR model. It has also been shown that by reducing the number density ratio of boron to protons from 0.3 to 0.2 and 0.1, due to the reduction of the coupling of electrons and ions, which leads to the reduction of collisions with Alpha particles, the stopping length of Alpha particles increases.

No street is an Island: Street network morphologies and traffic safety

Network morphological analysis has emerged as a tool to quantify street network structures, providing a nuanced foundation for evaluating their impacts on traffic safety. Yet, there is a lack of disaggregate-level evidence on the spillover effects and spatial heterogeneity of these impacts. This research conducts a comprehensive, disaggregate-level, multi-scale examination on the overall impacts of street network morphologies on traffic safety. Our study focuses on the frequency of traffic injury collisions over a five-year period across more than 190,000 street links in Greater London. We characterise street-link morphologies at local (0–1 km), meso (0–3 km), and city (0–8 km) scales using a spatial design network analysis. For each spatial scale, we apply extended auto-negative binomial models to examine the overall impact of street-link morphological characteristics on the injury collision frequency, considering both the link being investigated and other surrounding links determined by the spatial scale.We find significant spatial heterogeneity in the overall safety impacts of street-link morphologies. At the local scale, higher farness of a street link corresponds to an overall increase in injury collisions, whereas at the meso and city scales, it indicates an overall decrease. At the local and meso scales, higher betweenness of a street link is associated with an overall increase in injury collisions, but at the city scale, it correlates with an overall decrease. Independent of the spatial scale, a larger diversion ratio of a street link is linked to an overall decrease in injury collisions. These findings are similar to those on killed and seriously injured-only collisions. Our findings suggest that encouraging compact street network structures, which aligns well with New Urbanism and the Compact City policy, may not necessarily be effective for an overall reduction in injury collisions across an entire city.

Major trauma presentations and patient outcomes in English hospitals during the COVID-19 pandemic: An observational cohort study.

Single-centre studies suggest that successive Coronavirus Disease 2019 (COVID-19)-related "lockdown" restrictions in England may have led to significant changes in the characteristics of major trauma patients. There is also evidence from other countries that diversion of intensive care capacity and other healthcare resources to treating patients with COVID-19 may have impacted on outcomes for major trauma patients. We aimed to assess the impact of the COVID-19 pandemic on the number, characteristics, care pathways, and outcomes of major trauma patients presenting to hospitals in England. We completed an observational cohort study and interrupted time series analysis including all patients eligible for inclusion in England in the national clinical audit for major trauma presenting between 1 January 2017 and 31 of August 2021 (354,202 patients). Demographic characteristics (age, sex, physiology, and injury severity) and clinical pathways of major trauma patients in the first lockdown (17,510 patients) and second lockdown (38,262 patients) were compared to pre-COVID-19 periods in 2018 to 2019 (comparator period 1: 22,243 patients; comparator period 2: 18,099 patients). Discontinuities in trends for weekly estimated excess survival rate were estimated when lockdown measures were introduced using segmented linear regression. The first lockdown had a larger associated reduction in numbers of major trauma patients (-4,733 (21%)) compared to the pre-COVID period than the second lockdown (-2,754 (6.7%)). The largest reductions observed were in numbers of people injured in road traffic collisions excepting cyclists where numbers increased. During the second lockdown, there were increases in the numbers of people injured aged 65 and over (665 (3%)) and 85 and over (828 (9.3%)). In the second week of March 2020, there was a reduction in level of major trauma excess survival rate (-1.71%; 95% CI: -2.76% to -0.66%) associated with the first lockdown. This was followed by a weekly trend of improving survival until the lifting of restrictions in July 2020 (0.25; 95% CI: 0.14 to 0.35). Limitations include eligibility criteria for inclusion to the audit and COVID status of patients not being recorded. This national evaluation of the impact of COVID on major trauma presentations to English hospitals has observed important public health findings: The large reduction in overall numbers injured has been primarily driven by reductions in road traffic collisions, while numbers of older people injured at home increased over the second lockdown. Future research is needed to better understand the initial reduction in likelihood of survival after major trauma observed with the implementation of the first lockdown.

Improving Truck Driver and Vulnerable Road User Interactions Through Driver Training: An Interview Study With Canadian Subject Matter Experts

Collisions between trucks and vulnerable road users (VRUs) represent one of the most severe types of road collisions. Research initiatives to mitigate truck–VRU collisions include vehicle redesign, infrastructure improvements, and driver warning systems. Truck driver training is a complementary solution that can lead to improved driving behavior and collision reduction. We aimed to understand the perceptions of subject matter experts of current truck driver training in Ontario, Canada, to identify gaps and potential improvements, particularly targeting VRU safety in urban areas. Further, we investigated the degree to which VRU safety is covered and the potential to incorporate it in training through simulators. We conducted semi-structured interviews with 21 truck driver trainees, novice and experienced truck drivers, a driving instructor, and road safety professionals. The participants highlighted a notable gap between current training and real-world truck driving. VRU safety and hazard anticipation training emerged as a missing component in training. Nine out of 11 participants who received simulator training perceived it positively and recommended it for its safe and relatively realistic environment. The main topics that should be incorporated in VRU safety training, as recommended by interviewees, include training on difficult truck maneuvers with presence of VRUs, anticipating hazardous VRU actions, and navigating difficult infrastructure components. This work presents subject matter expert perceptions of current truck driver training in Ontario, Canada, and identifies gaps and improvements targeting VRU safety. Although the study is based on Ontario, the results can apply across Canada and beyond, where trucks pose dangers to VRUs.

Impacts of Connected and Automated Vehicles on Road Safety and Efficiency: A Systematic Literature Review

Connected and automated vehicles (CAVs), in the context of cooperative intelligent transportation systems (C-ITS), are capable of exchanging information with each other and the infrastructure. They can also form platoons that can drive with shorter time headways resulting in potential higher capacity, smoother flow, less fuel consumption, and reduction in collisions. However, the literature assessing their impacts is still fragmented and does not provide a solid evidence base about their potential contributions, particularly that they are not widely deployed yet. This study aims to establish this evidence using a systematic literature review (SLR) of the relevant body of knowledge. In this paper, 347 journal publications from 2000 to 2021, are analyzed and critiqued. Comprehensive bibliographic and co-citation analyses are conducted to uncover the key findings and bring out a valid and balanced perspective of CAVs and their impacts on traffic operations. A co-citation analysis of the body of knowledge on this topic resulted in the formation of four main clusters of research focus that included safety, efficiency, communication, and technology. This SLR presents an unbiased and objective summary of scientific findings on the topic and serves as a starting point for future investigations on CAVs by identifying research gaps, limitations and challenges, and potential areas of research to overcome these challenges.

Design of Unsignalized Roundabouts Driving Policy of Autonomous Vehicles Using Deep Reinforcement Learning

Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that potentially have conflicts with the ego-vehicle, while guessing the intentions of other obstacle vehicles. In this paper, a driving policy based on the Soft actor-critic (SAC) algorithm combined with interval prediction and self-attention mechanism is proposed to achieve safe driving of ego-vehicle at unsignalized roundabouts. The objective of this work is to simulate a roundabout scenario and train the proposed algorithm in a low-dimensional environment, and then test and validate the policy in the CARLA simulator to ensure safety while reducing costs. By using a self-attention network and interval prediction algorithms to enable ego-vehicle to focus on more temporal and spatial features, the risk of driving into and out of the roundabout is predicted, and safe and effective driving decisions are made. Simulation results show that our proposed driving policy can provide collision risk avoidance and improve vehicle driving safety, resulting in a 15% reduction in collisions. Finally, the trained model is transferred to the complete vehicle system of CARLA to validate the possibility of real-world deployment of the policy model.