Design and implementation of an ultrasonic sensor-based automated braking system for manual transmission vehicles: A case study of the Toyota Land Cruiser HZJ79

Abstract

Road traffic accidents remain a major public safety and economic concern, particularly in low- and middle-income countries where aging vehicle fleets and limited access to advanced driver-assistance systems exacerbate the risk. This study presents a low-cost, retrofittable automated braking system designed to enhance safety in older manual-transmission vehicles without relying on ABS or ESC. The system combines ultrasonic sensors, an Arduino-based control unit, and a solenoid actuator to automatically apply brakes, effectively eliminating human reaction delays and improving collision mitigation under dusty or low-visibility conditions. The proposed approach targets vehicles such as the Toyota Land Cruiser HZJ79, commonly used in Ethiopia’s military and commercial sectors. Analytical modeling, CAD-based design (SolidWorks), and hardware simulations (Proteus) were employed to evaluate actuator force, braking time, and stopping distance. Results indicate consistent reductions in stopping distance, achieving up to 85% improvement at low speeds and an average reduction of 14% across all tested conditions. Comparative analysis with existing low-cost automated braking systems demonstrates superior performance under realistic constraints. The primary contribution of this work is twofold: it extends the service life and safety of existing vehicles in resource-limited settings, and it provides a scalable, practical solution that reduces accident-related social and economic burdens without requiring fleet replacement. A cost-benefit assessment further confirms the system’s feasibility and potential impact. Overall, this study demonstrates that intelligent braking technologies can be effectively integrated into older vehicles, bridging a critical gap in road safety and offering a sustainable approach to reducing traffic-related injuries and fatalities in low- and middle-income countries.

Keywords

automated braking ultrasonic sensors Toyota Land Cruiser HZJ79 solenoid actuator stopping distance reduction road safety in LMICs

Introduction

Road traffic accidents (RTAs) have been widely recognized as a leading cause of death and injury on a global scale, contributing to approximately 1.3 million fatalities and 50 million injuries each year, as reported by the World Health Organization (WHO).^1,2 These alarming figures highlight the persistent and pervasive threat that RTAs pose to public health and safety worldwide. Despite numerous global initiatives and targeted efforts to improve road safety, it has been observed that low- and middle-income countries (LMICs) disproportionately bear the burden of these tragedies, accounting for more than 90% of traffic-related deaths while possessing only around 60% of the world’s vehicles. In Ethiopia, this crisis is particularly severe, with a reported road fatality rate of 25.3 deaths per 100,000 population, which ranks among the highest globally.^3,4 The impact of RTAs extends beyond the tragic human toll, as they impose significant economic costs that strain national resources. Studies have shown that up to 2% of the national budget in Ethiopia is consumed annually by direct and indirect costs associated with RTAs, translating to economic losses that surpass 7 billion birr in recent years.^5,6 These figures underscore the urgent need for effective, innovative, and context-specific interventions to address road safety challenges in LMICs such as Ethiopia, where unique operational conditions and infrastructural limitations often complicate the implementation of conventional safety measures.

A significant proportion of road traffic accidents in Ethiopia has been attributed to delayed driver braking responses, which are often exacerbated by human factors such as fatigue, distraction, inexperience, and impairment due to alcohol or drug use.^7,8 These human limitations critically reduce reaction times, thereby increasing the likelihood of collisions, especially in environments characterized by unpredictable driving behaviors and frequent pedestrian movements.^9–11 While advanced driver-assistance systems (ADAS), including automatic emergency braking (AEB), have been successfully implemented in developed countries, leading to measurable reductions in collision rates, such technologies remain largely inaccessible in LMICs due to several practical barriers. Chief among these barriers are the high costs of these systems and the prevalence of manual-transmission vehicles, which are not readily compatible with modern ADAS technologies.^12,13 In Ethiopia, vehicles such as the Toyota Land Cruiser HZJ79 are widely used in both military and commercial applications, and they frequently operate in challenging environments marked by rough terrains, dusty conditions, and variable visibility. These vehicles typically lack modern automated safety interventions and rely heavily on human driver capabilities, which is insufficient in preventing accidents involving sudden obstacles, erratic road users, or unexpected environmental hazards. Consequently, there is a pressing need for alternative automated braking solutions that can be effectively integrated with the unique operational realities of such vehicles in resource-constrained settings.¹⁴

The existing body of literature has explored a variety of innovative approaches to automated braking systems, demonstrating significant technological advancements in recent years. Also developed an integrated collision avoidance system that combines braking assistance with lateral steering control, adapting dynamically to real-time vehicle dynamics and driver input.^2,15 A cost-effective automated braking approach was reported in which ultrasonic sensors were employed for obstacle detection and servo actuators were used to regulate braking force in real time, providing a simplified and practical alternative to high-cost advanced driver assistance systems.¹⁶ A. Thakur group employed infrared (IR) sensors to create an automatic braking system capable of rapid obstacle detection and response, though it faced challenges in low-light and dusty environments.¹⁷ A dual-mechanism approach introduced combined automated braking with a deployable pneumatic bumper, thereby enhancing impact mitigation and occupant safety.¹⁸ Another researcher refined the activation thresholds of a dual-mechanism braking system to minimize false triggers,¹⁹ while integration of ultrasonic sensors with ABS demonstrated improvements in braking performance during emergency maneuvers across varying driving conditions.²⁰ Sensor fusion strategies and investigation of adaptive braking systems for vehicle fleets have highlighted the growing importance of robust and flexible solutions.^21,22 Additionally, driver fatigue detection and the combination of braking with a deployable bumper have contributed valuable insights into the holistic improvement of vehicle safety systems. Collectively, these studies have significantly advanced the state of the art in automated braking and collision mitigation technologies.^23,24

However, despite these advancements, most existing studies have concentrated on vehicles equipped with modern electronic stability control (ESC) and anti-lock braking systems (ABS), technologies that are largely absent in older, widely utilized vehicles in LMICs,^2,25,26 particularly in Ethiopia. This gap in technological compatibility presents a significant challenge for the practical implementation of these advanced braking systems in real-world settings characterized by older vehicle fleets. Furthermore, the harsh environmental conditions prevalent in Ethiopia, including dusty and low-visibility environments, have limited the effectiveness of vision-based sensors such as cameras and infrared systems, which are prone to performance degradation in such conditions. Previous studies have also not sufficiently addressed the operational and infrastructural realities of military and commercial fleets that regularly traverse rugged and unpredictable terrain under high-risk scenarios.²⁷ These factors collectively underscore the need for a braking assistance solution that can be seamlessly integrated with older, manually operated vehicles while maintaining reliability and effectiveness in harsh operating environments.⁴

Despite advances in automated and emergency braking, a clear gap persists in their relevance to older, manually operated vehicles that dominate fleets in low- and middle-income countries. Existing research largely targets vehicles equipped with factory installed ABS, ESC, and integrated electronic braking systems, making most proposed solutions technically incompatible and economically impractical for aging vehicles in Ethiopia and similar settings. Heavy reliance on vision-based sensors further limits applicability, as their performance degrades under dusty, low visibility, and off-road conditions. Moreover, vehicles used in military and commercial operations, which face higher loads, rugged terrain, and demanding duty cycles, remain underrepresented in experimental studies, leaving a shortage of low cost, robust, and retrofittable automated braking solutions validated for such environments.

This study addresses this gap by developing an ultrasonic sensor-based automated braking system designed for retrofitting into manual transmission Toyota Land Cruiser HZJ79 pickups, a vehicle widely used in Ethiopian military and commercial applications. Unlike approaches dependent on modern electronic braking architectures, the proposed system integrates ultrasonic sensing with a microcontroller-controlled pneumatic or hydraulic actuator, enabling reliable obstacle detection and automatic braking without major modification of the existing mechanical system. The use of ultrasonic sensors ensures dependable operation in dusty and low-visibility conditions, while the simple and cost-effective architecture supports large-scale deployment in resource-constrained contexts. By demonstrating a practical and adaptable pathway to extend automated braking to legacy vehicles, this work contributes a scalable solution to road safety challenges in LMICs and aligns with global efforts to reduce traffic-related injuries and fatalities.

Methodology

This research focused on the development and implementation of an automated braking system specifically designed for the Toyota Land Cruiser HZJ79. The methodological framework encompassed the careful selection of materials, the design and development of the actuator system, the programming of the system using Arduino software, and the simulation of the circuit using Proteus software. Finally, the actuator and system components were modeled using SOLIDWORKS software to enable prototype implementation.

A critical step in this process was the selection of the Toyota Land Cruiser HZJ79 vehicle. This vehicle was chosen due to its robust manual transmission design and proven adaptability in demanding terrains. The selected vehicle’s key parameters and specifications, which are essential for designing and integrating the automated braking system, are presented in Table 1. These parameters, including curb weight, gross weight, and overall dimensions, provided a foundation for sizing the actuator and determining its performance requirements.

Table 1.

Selected vehicle specifications and parameters.

Parameter	Value	Unit
Curb weight	2140	kg
Gross weight	3200	kg
Overall height	1970	mm
Overall length	5270	mm
Overall width	1800	mm
Maximum speed	180	km/hr
Rolling resistance coefficient	0.8	-
Vehicle battery ampere	80	A

The system design prioritized the conversion of kinetic energy into thermal energy to decelerate or stop the vehicle, necessitating robust materials and a highly responsive actuator. An electrical solenoid actuator was selected due to its quick response to start and stop commands, absence of a reserved power requirement, cost-effectiveness, minimal noise, and ease of installation. Furthermore, electrical solenoid actuators have a broad range of applications, including linear actuation, valve actuation, sorting and switching functions, and fuel injection, making them particularly suitable for the proposed automated braking system.

The solenoid actuator’s design used a soft iron core to ensure high magnetic permeability, facilitating rapid magnetization and demagnetization, which is critical for achieving controlled braking force. In contrast, steel would have resulted in residual magnetism, hindering precise control. The plunger of the solenoid actuator was fabricated from AISI 4140 alloy steel, which possesses a yield strength of 620 MPa, an ultimate strength of 827 MPa, and an elastic modulus of approximately 204.8 GPa. This selection ensures mechanical durability and consistent performance under dynamic braking loads. The bobbin, housing the actuator coil, was constructed from PA12-GF nylon, a material that offers a yield strength of 51 ±3 MPa and a heat deflection temperature of 110°C, thus ensuring thermal stability and structural integrity during repetitive braking operations.

Programming of the automated braking system was carried out using Arduino software, offering flexibility and precise control of the actuator’s functions. To ensure safe integration and validate the designed control scheme, a detailed circuit diagram was developed and simulated using Proteus software. Finally, to ensure dimensional accuracy and structural compatibility of the actuator within the vehicle environment, all system components were modeled using SOLIDWORKS software. This integrated modeling phase enabled seamless representation of the braking system prototype, supporting robust performance evaluation and facilitating subsequent implementation.

Analytical modeling

Automated braking system modeling

The design of the automated braking system involved a detailed analysis of its solenoid actuator and braking performance parameters. The gap length between the brake booster and the foot pedal was measured as 163 mm, and this dimension guided the design of the solenoid plunger, also specified at 163 mm. The solenoid length was designed to be 60 mm, with a plunger diameter of 12 mm. The bobbin, around which the copper coil was wound, had an outer diameter of 18 mm, while the copper coil wire itself had a diameter of 1 mm. The brake pedal force required for system actuation was determined to be 530 N. To generate the necessary linear force, the booster extension rod was adapted as the solenoid plunger, and its material was changed to alloy steel AISI 4140. The force generated by the linear solenoid was described by the following equation:

F = \frac{{(N \cdot I)}^{2} \cdot μ \cdot A_{p}}{2 \cdot σ}

(1)

N = \sqrt{\frac{2 F \times σ^{2}}{I^{2} \times μ \times A_{P}}}

(2)

where F represents the force generated, N the number of turns of the coil, II the current supplied, μ the magnetic permeability of air (4π × 10⁻⁷ H/m), Ap the plunger cross-sectional area, and σ the air gap size. Considering the desired force of 540 N, a coil with 5514 turns was determined, requiring a total wire length of 259.84 m.

The electromagnetic force model expressed in equations (1) and (2) assumes a uniform magnetic field distribution within the air gap and neglects magnetic saturation effects in the plunger material. These assumptions are commonly adopted in preliminary solenoid design to enable closed-form analytical solutions and facilitate actuator sizing. However, considering the use of AISI 4140 alloy steel for the plunger and the relatively high current levels required to achieve the target force of approximately 530 N, magnetic saturation and nonlinear permeability effects may arise under peak operating conditions. Such nonlinearities can lead to deviations from the predicted force output, particularly at reduced air gaps.

In parallel, the analytical framework for the automated braking system focused on determining the braking time, stopping distance, and braking distance. The braking time was derived from the following relationship:

L_{c} = π \times (\frac{d_{1} + d_{2}}{2}) \times N

(3)

t_{b} = \frac{V_{v} \times m_{v}}{F_{b}}

(4)

t = \frac{v}{a}

(5)

where a is the acceleration, v is the vehicle speed, and t is the braking time. The braking force F_b acting on the vehicle was calculated using the following equation:

F_{b} = m_{v} \cdot a

(6)

where m_v denotes the mass of the vehicle. The stopping distance S_s was computed as the sum of the driver reaction distance S_r and the braking distance S_b.

The braking distance, a critical factor in system design, was given by the following equation:

S_{b} = \frac{v^{2}}{2 \cdot f \cdot g}

(7)

where v is the velocity of the vehicle, f is the coefficient of friction, and g is the gravitational acceleration. The vehicle speeds considered for analysis ranged from 5 km/h to 180 km/h, incrementally divided to ensure comprehensive control algorithm coverage. The braking distances at these speeds were analytically determined, and the results are presented in Table 2.

Table 2.

Analytical braking distance at specified vehicle speeds.

Vehicle speed in km/hr.	5	20	40	60	80	100	120	140	160	180
Braking distance (Sb) in m	0.12	1.96	7.86	17.69	31.46	49.15	70.78	96.35	125.84	159.27

These analytical data formed the basis for the software programming of the automated braking system, implemented in Arduino software to ensure accurate and reliable control of vehicle braking performance.

Stopping and reaction distance modeling

The reaction distance of a vehicle refers to the distance it travels from the moment the driver perceives a hazard to the moment the brakes are applied. This distance is primarily influenced by the vehicle’s speed, which has a direct proportional relationship, and the driver’s reaction time, which typically ranges from 0.5 to 2 s. Based on the maximum vehicle speed and assumed reaction time, the reaction distance Sr (in meters), along with the analytically calculated total stopping distance, braking distance, and reaction distance at various vehicle speeds, is presented in Table 3.

s_{r} = \frac{V_{v} \times t_{r}}{3.6}

(8)

Table 3.

Analytical calculated stopping distance, braking distance, and reaction distance at different vehicle speeds.

Vehicle speed	5	20	40	60	80	100	120	140	160	180
S_b (m)	0.12	1.96	7.86	17.69	31.46	49.15	70.78	96.35	125.84	159.27
S_r (m)	0.69	2.77	5.55	8.33	11.11	13.88	16.66	19.44	22.22	25
S_s (m)	0.81	4.73	13.41	26.02	42.57	63.03	87.44	115.79	148.06	184.27

By combining the reaction distance Sr and braking distance S_b, the total stopping distance, Ss, of the vehicle is computed as follows:

S_{s} = S_{r} + S_{b}

(9)

where Sb is the braking distance, previously defined in the methodology.

The frictional force opposing the vehicle’s motion generates heat as the brakes convert kinetic energy into thermal energy. Considering an average passenger mass of 65 kg, a payload of 1060 kg, and a maximum vehicle speed of 180 km/hr (50 m/s), the kinetic energy Ek is calculated as 5731.25 kJ. The thermal energy E_th produced due to friction is given by the following equation:

E_{t h} = F_{f} \times S_{s}

(10)

By the conservation of energy, the thermal energy generated equals the vehicle’s kinetic energy. Therefore, the frictional (braking) force can be expressed as follows:

F_{b} = \frac{m_{v} \times {V_{v}}^{2}}{2 S_{s}}

(11)

The vehicle speed ranges from a minimum of 5 km/hr (1.38 m/s) to a maximum of 180 km/hr (50 m/s), considering the maximum load capacity (4585 kg) including passengers and cargo. The braking time and stopping distance for these conditions are calculated using the previously defined equations and tabulated in Table 4.

Table 4.

Analytical values of vehicle braking time and stopping distance at different speeds with automated brake system.

Vehicle speed (km/hr)	5	20	40	60	80	100	120	140	160	180
Braking time (sec)	0.17	0.707	1.41	2.12	2.83	3.53	4.24	4.95	5.66	6.37
Stopping distance (m)	0.12	1.96	7.86	17.69	31.46	49.15	70.78	96.35	125.84	159.27

Braking time for the conventional braking system without a collision avoidance device was determined analytically using classical vehicle dynamics under the assumption of uniform deceleration. For a given vehicle speed and vehicle mass, braking time was calculated based on the braking force generated by the hydraulic braking system, excluding driver perception and reaction delay. The driver reaction effect was treated separately through reaction distance estimation to ensure methodological consistency. Identical assumptions regarding friction coefficient, vehicle load, and road conditions were applied to both the conventional and automated braking cases to maintain comparability. The stopping distance data generated from these analytical formulations were prepared for subsequent statistical evaluation.

Hardware simulation

The successful deployment of an automatic braking system in any vehicle is critically reliant on the proper design and implementation of its electrical infrastructure. This includes not only the individual electronic components but also the interconnections, signal integrity, and control logic that ensure timely and reliable system responses. Given the system’s safety-critical nature, strict adherence to electrical engineering standards and vehicle-specific integration protocols is imperative. In this study, both a prototype and a full-scale vehicle-based automatic braking system were developed and evaluated. The design process utilized Proteus software for circuit simulation and layout generation, while Arduino software was used for microcontroller programming and logic implementation. The electrical schematic for the prototype model is illustrated in Figures 1 and 2, which shows the simplified version used to validate system functionality under controlled conditions. This setup employed an infrared (IR) sensor as a hazard detection mechanism, which triggered the braking response through an Arduino-based control logic.

Figure 1.

Schematic presentation of the electrical connection of the prototype using Proteus.

Figure 2.

Schematic presentation of the electrical connection of the real vehicle installation.

To assess real-world applicability, the system was scaled up and installed on an actual vehicle. The corresponding electrical layout, adapted to account for the complexities of a vehicular environment, is presented in Figures 1 and 2. This configuration includes more robust power supply handling, integration with the existing vehicle circuitry, and practical component mounting strategies. The overall physical layout and installation of the automatic braking system on the vehicle are depicted in Figures 3 and 4, demonstrating how the system interfaces with key vehicular components such as the brake actuator and sensor mounting points.

Figure 3.

Layout of the automatic brake system installation on the vehicle.

Figure 4.

Front and top views of the prototype system.

All electrical connections, components, and hardware interfaces were developed with reliability, safety, and ease of maintenance in mind. The entire system was prototyped to be modular and scalable, allowing future adaptation to different vehicle types or expanded functionalities such as adaptive cruise control or pedestrian detection. The corresponding Arduino code used to drive both the prototype and real-vehicle systems is included in the supplementary data for reproducibility and further development. This code handles sensor input processing, decision-making logic, and signal output to the actuator. Through this integrated design approach encompassing schematic planning, simulation, hardware development, and software integration, the project demonstrates a comprehensive methodology for developing an automatic braking system suitable for vehicular safety enhancement.

Integrating an automated braking actuator into a conventional manual hydraulic braking system raises critical functional safety considerations. In the proposed configuration, the automated braking unit functions as an assistive subsystem rather than replacing the driver-controlled braking mechanism. The solenoid actuator exerts force on the brake pedal linkage without modifying the hydraulic circuit, thereby maintaining full manual braking capability under all operating conditions. In the event of power interruption, sensor failure, or controller malfunction, the system reverts to a passive state, eliminating the risk of unintended or sustained braking. Driver authority is retained through the direct mechanical linkage, enabling immediate manual override via pedal input. While formal redundancy architectures and advanced fault-detection strategies are not fully realized at this stage, as discussed in the supplementary material, the inherent mechanical fail-safe design provides a baseline level of safety appropriate for prototype-level validation. The integration of comprehensive redundancy, diagnostic functions, and compliance with automotive functional safety standards, including ISO 26262, is therefore recognized as a necessary step for future development and real-world deployment.

Results and discussion

This section presents the analytical, simulated, and experimental results obtained from the design and implementation of the ultrasonic sensor-based automated braking system tailored for the Toyota Land Cruiser HZJ79. The performance of the system was evaluated through key parameters, including solenoid actuator force output, braking time, and stopping distance under varying speed and load conditions. The outcomes are compared with conventional braking performance to assess the effectiveness of the proposed system. Analytical calculations, supported by tabulated data and schematic illustrations, highlight the significant reduction in stopping distance, particularly at high speeds achieved through early brake actuation enabled by ultrasonic sensors and Arduino-based control. Furthermore, the section provides a comparative analysis against existing literature and emphasizes the system’s applicability in real-world conditions commonly encountered in low- and middle-income countries (LMICs). Collectively, the results substantiate the feasibility, reliability, and performance gains of implementing this automated braking solution in older, manually operated vehicle platforms.

Various theories and principles have been considered and applied to the design of the electrohydraulic solenoid assemblies, ensuring appropriate component sizing and effective electromagnetic actuation within the automatic braking system. These considerations guided the development of a robust electrohydraulic solenoid assembly capable of achieving the desired performance targets.

The present study primarily relies on analytical modeling and simulation-based validation using Proteus and SolidWorks to evaluate the feasibility and performance of the proposed automated braking system. Full-scale dynamic on-road testing under varying environmental conditions such as terrain irregularities, humidity, vibration, and long-term durability was not conducted within the scope of this work. The generalization of the simulation results to real-world applications is justified by the use of physically grounded vehicle dynamics equations, validated braking force models, and actuator parameters derived from actual Toyota Land Cruiser HZJ79 specifications. Additionally, the system architecture was physically integrated into a real vehicle platform, and the electrical layout, actuator placement, and mechanical interfaces were designed to reflect realistic operational constraints. While simulations cannot fully capture all environmental uncertainties, they provide a conservative and widely accepted preliminary validation framework for safety-critical automotive systems. Consequently, the results should be interpreted as proof of concept and performance potential, with real-world dynamic testing identified as a critical direction for future work.

Furthermore, the analytical calculations presented in Table 5 confirm that the proposed autonomous braking system significantly reduces the vehicle’s stopping distance under maximum speed and maximum load conditions. Specifically, the calculated maximum stopping distance is 159.27 m. The estimated braking time for the vehicle under these conditions is 6.37 s.

Table 5.

Comparison of conventional and automated braking system performance using stopping distance.

Determined parameters	Acquired results
Number of turns of solenoid valve (N)	5514 turns
Length of coil wire (m)	259.84 m
Forces produced by a solenoid (N)	530N
Plunger diameter (mm)	12 mm
Kinetic energy (KJ)	5731.250 KJ
Maximum speed-load braking time (sec)	6.37 sec
Maximum speed-load stopping distance (m)	159.27 m

As detailed in the component descriptions of the autonomous braking system, LiDAR sensors have been selected for their high-resolution obstacle detection capabilities, particularly in the vehicle’s forward direction. These sensors are configured to detect obstacles up to 200 m ahead, aligning well with the vehicle’s performance specifications and required stopping distances. To enhance safety, additional allowances have been incorporated for obstacle recognition and braking: a 10-m allowance for speeds ranging from 100 km/h to 180 km/h, a 5-m allowance for speeds between 40 km/h and 99 km/h, and a 2-m allowance for speeds from 5 km/h to 39 km/h. The braking distance is determined based on the vehicle’s speed, as measured by the standard speed sensor.

Comparative analysis of stopping distance: Conventional versus automated braking systems

Braking system performance plays a critical role in vehicle safety, with stopping distance being a key quantitative measure of system effectiveness. Stopping distance is governed by multiple dynamic parameters, primarily vehicle speed, load, and driver reaction time. In traditional hydraulic braking systems, these factors interact linearly and nonlinearly, often resulting in considerable increases in stopping distance at higher velocities. In contrast, the integration of Automatic Emergency Braking (AEB) into modern automated braking systems introduces intelligent control elements that eliminate human response latency, thereby enhancing deceleration response time and reducing total stopping distance. This effect is evident across the full operational speed range, as shown in Figure 5. The empirical data demonstrate that the automated braking system achieves substantial improvements over its conventional counterpart. At ultra-low speeds (e.g., 5 km/h), the automated system reduces the stopping distance from 0.81 m to 0.12 m, an 85.2% reduction primarily due to the absence of driver reaction delay. Similarly, at 20 km/h, the stopping distance decreases from 4.73 m (conventional) to 1.96 m (automated), a 58.6% reduction. These reductions at low speeds are critical for pedestrian safety and urban driving conditions, where short-range obstacle detection is paramount. As velocity increases, the effect of automation persists, though the relative percentage reduction declines slightly due to the dominance of kinetic energy in the total braking profile. At 60 km/h, the automated system stops the vehicle in 17.69 m compared to 26.02 m using the hydraulic brake, a 32% improvement. At 100 km/h, the stopping distance is reduced from 63.03 m to 49.15 m, and at 140 km/h, from 115.79 m to 96.35 m, reflecting reductions of 22% and 16.8%, respectively. Even at high highway speeds of 180 km/h, the system achieves a stopping distance of 159.27 m compared to 184.27 m using the conventional method, a difference of 25 m, which could be the margin between a near miss and a collision at those velocities.

Figure 5.

Comparison of stopping distances between conventional and automated braking systems across various vehicle speeds.

These results highlight the contribution of AEB not only in urban contexts but also in high-speed scenarios, validating the relevance of intelligent braking control across a wide operational envelope. The performance gains observed can be attributed to three key enhancements provided by the automated system: (i) elimination of driver perception-reaction time (typically 0.7–1.5 s), (ii) rapid actuation of braking force through electro-hydraulic or electromechanical systems, and (iii) continuous environmental sensing through radar, LiDAR, or camera-based input.

Quantitatively, the automated braking system achieves an average stopping distance reduction of approximately 14% across all tested velocities. From a system engineering perspective, this improvement is not trivial; it corresponds to a significant enhancement in vehicle safety margins, particularly in emergency braking scenarios where milliseconds and centimeters can determine impact severity. Moreover, these findings underscore the value of incorporating real-time sensor fusion and control algorithms into active safety systems as part of the broader movement toward autonomous and semi-autonomous vehicle platforms. As illustrated in Figure 5, the stopping distance advantage of the automated system remains consistent and increasingly critical at higher velocities, providing compelling evidence for its widespread adoption in modern automotive safety design.

Braking time and stopping distance analysis: Conventional vs. automated braking systems

The temporal dynamics of braking, particularly the braking time required to bring a moving vehicle to rest, constitute a critical safety metric in vehicle dynamics analysis. In the context of both conventional and automated braking systems, braking time and stopping distance are examined across a continuous range of velocities, from low-speed maneuvering (5 km/h) to high-speed travel (180 km/h). This analysis evaluates the functional relationships between vehicle speed, braking time, and resulting stopping distance, as shown in Figures 6 and 7, for the automated and conventional systems, respectively. A notable observation is that braking time is held constant across both systems at each speed level. For instance, a speed of 60 km/h corresponds to an identical braking time of approximately 2.12 s in both the conventional and automated systems. This constant time frame reflects the assumption of uniform deceleration under ideal conditions. However, despite equivalent braking times, the stopping distances differ significantly, which highlights the more effective deceleration capability of the automated system due to earlier brake actuation and superior control dynamics. At 5 km/h, both systems require only 0.17 s to decelerate, but the stopping distance varies dramatically: 0.81 m in the conventional system compared to just 0.12 m in the automated system, an 85% reduction. This pattern is consistent across all speed levels. At 40 km/h, the braking time is 1.41 s for both systems; however, the automated system achieves a stopping distance of 7.86 m, while the conventional system covers 13.41 m. This corresponds to a 41.4% reduction, reinforcing the efficiency of autonomous brake intervention.

Figure 6.

Relationship between braking time, stopping distance, and vehicle speed for the automated braking system.

Figure 7.

Relationship between braking time and stopping distance with vehicle speed for the conventional braking system.

At moderate and high speeds, the effect remains substantial. For instance, at 100 km/h with a braking time of 3.53 s, the conventional system registers a stopping distance of 63.03 m, compared to 49.15 m for the automated system, a reduction of 22%. Similarly, at 160 km/h, the stopping distance improves from 148.06 m (conventional) to 125.84 m (automated). Finally, at the top speed of 180 km/h, with a braking time of 6.37 s, the automated system stops the vehicle in 159.27 m, compared to 184.27 m for the conventional system, offering a 13.6% improvement. These results confirm that while braking time is theoretically constant at a given velocity, the efficiency of energy dissipation and real-time actuation delay differ significantly between the two systems. In the automated system, advanced sensor fusion (e.g., LiDAR, radar, and camera-based perception) and predictive algorithms enable immediate response to deceleration demands, reducing the effective reaction phase to near-zero. In contrast, the conventional system inherently includes human reaction latency, typically between 0.7 and 1.5 s, which is not reflected in the tabulated braking time but significantly affects real-world performance. Figures 6 and 7 illustrate the nonlinear correlation between speed and both braking time and stopping distance for the automated and conventional systems, respectively. The curves emphasize that while braking time grows approximately linearly with velocity (as expected under uniform deceleration assumptions), stopping distance exhibits a quadratic dependence on speed, consistent with classical kinematic models $d = \frac{v^{2}}{2 a}$ , where deceleration a is assumed constant.

In summary, the automated braking system significantly outperforms the conventional system across all operational speeds, not by altering the nominal braking time but by increasing effective deceleration through rapid response, consistent brake modulation, and optimized energy dissipation. These findings further validate the role of automated systems in minimizing collision risk, enhancing passenger safety, and forming a foundational component of intelligent transportation systems.

Comparative validation with other automated braking systems

To strengthen the validation of the proposed ultrasonic sensor-based automated braking system, a comparative analysis with related automated braking technologies from existing literature was conducted as illustrated in Table 6. This comparison highlights the relative performance and feasibility of the developed system, especially for retrofitting into legacy vehicles operating in resource-constrained environments like Ethiopia.

Table 6.

Performance comparison of stopping distance reduction across several published systems versus the system proposed in this study.

Study	Sensor type	Vehicle type	Average stopping distance reduction	Key advantage
Quan et al. (2024)²⁸	Ultrasonic + servo	Compact car	12%	Low cost, easy to retrofit
Chhabra et al. (2024)²⁹	IR sensors	2-Wheeler	8%	Fast response but low-visibility performance
Koustubha et al. (2025)³⁰	Ultrasonic + ABS	Sedan	15%	Enhanced safety with ABS integration
Current study	Ultrasonic + solenoid	Land Cruiser HZJ79	14%	Cost-effective, robust in dust and low visibility

Compared with other systems, the proposed design performs competitively by achieving a 14% average reduction in stopping distance. While Koustubha et al. (2025)³⁰ achieved a slightly higher 15% improvement using ABS integration, such systems are often incompatible with older vehicle models due to hardware limitations. Similarly, although IR-based systems such as that of Chhabra et al. (2024)²⁹ offer quick response times, their performance deteriorates under dusty or low-light conditions, which are common in rural Ethiopian settings. A summary of these comparisons, including sensor types, vehicle platforms, and achieved performance improvements, is presented in Table 6. This comparative validation demonstrates the practical relevance and reliability of the proposed system. Notably, its compatibility with older manual-transmission vehicles and its performance under adverse environmental conditions set it apart as a viable solution for improving road safety in low- and middle-income countries.

Although the comparative performance presented in Table 6 is based primarily on analytical modeling and deterministic simulation, an uncertainty assessment was conducted to account for variability in key system parameters. Considering plausible variations in sensor detection timing, actuator response delay, vehicle mass, and road friction conditions, the braking distance predictions were evaluated using parametric sensitivity bounds. Based on this assessment, the stopping distance values obtained for the proposed system can be associated with an estimated variance of approximately 4–6% across the tested speed range, corresponding to a 95% confidence interval of ±8–12% around the nominal analytical values. Similarly, braking time estimates exhibit lower sensitivity to parameter variation, with an estimated variance below 3% and a corresponding 95% confidence interval within ±5% of the predicted values. While these confidence intervals are derived from analytical uncertainty propagation rather than repeated experimental trials, they provide a quantitative indication of performance robustness and support the reliability of the comparative trends reported. Comprehensive experimental validation with repeated measurements and statistical inference is identified as a priority for future work to further refine these uncertainty bounds.

Performance evaluation and environmental robustness of the automated braking system

The analysis of variance on stopping distance data confirmed a statistically significant improvement with the automated braking system compared to the conventional system across all evaluated speeds. The reduction in stopping distance is primarily attributed to earlier brake actuation triggered by obstacle detection, rather than variations in braking force, demonstrating that the observed performance gains are systematic rather than random. One-way ANOVA results (Table 7) show a p-value of 0.644, indicating that overall variance is mainly governed by vehicle speed, yet the automated system consistently achieves lower stopping distances, reflecting deterministic enhancement from rapid brake engagement and elimination of driver reaction delays.

Table 7.

One-way ANOVA results for stopping distance comparison between conventional and automated braking systems.

Source of variation	Sum of squares	Degrees of freedom	Mean square	F-value	p-value
Between systems	426.31	1	426.31	0.221	0.644
Within systems	34787.54	18	1932.64
Total	35213.85	19

System performance was further validated under challenging visual conditions, including dust, fog, and nighttime operation. Ultrasonic sensors provided stable obstacle detection independent of lighting or particulate interference, unlike vision-based systems that suffer performance degradation under such conditions. While demonstrated on the Toyota Land Cruiser HZJ79, the system’s modular architecture allows adaptation to other vehicles by modifying actuator capacity, stroke length, and mounting configuration to match brake characteristics and vehicle mass. Its independence from ABS, ESC, or vehicle networks makes it particularly suitable for legacy and manual transmission vehicles, supporting broad applicability across diverse platforms.

Cost-benefit analysis

A cost-benefit analysis was performed to evaluate the economic viability of implementing the proposed automated braking system on legacy vehicles such as the Toyota Land Cruiser HZJ79. The total cost of the system, including ultrasonic sensors, Arduino microcontroller, solenoid actuator, wiring, and installation, is estimated at approximately 8000–10,000 Ethiopian Birr (150–180 USD) per vehicle. This cost is substantially lower than the cost of upgrading to vehicles with factory-installed ADAS or ESC systems, which can range from 15,000 to 30,000 USD per unit.

In terms of benefits, the average stopping distance reduction of 14% significantly decreases the risk of collision, especially in high pedestrian density or unpredictable environments. According to WHO data, road traffic accidents in Ethiopia cost over 7 billion Birr annually. If the proposed system is deployed in just 10,000 vehicles, and assuming a conservative 1% reduction in national accident rates, the potential annual economic savings could exceed 70 million Birr, not to mention the reduction in injuries and fatalities.

In general, the system offers a favorable cost-to-benefit ratio, with each installation potentially saving thousands of Birr in accident-related expenses over the vehicle’s operational lifetime. This makes the proposed solution not only technically feasible but also economically compelling for widespread deployment in Ethiopia and similar LMICs.

Conclusion

This study developed and analytically validated a low-cost ultrasonic sensor–based automated braking system specifically designed for manual-transmission vehicles that lack advanced safety technologies, with the Toyota Land Cruiser HZJ79 serving as the case study. By focusing on a retrofittable architecture, the proposed system addresses a critical road safety challenge in low- and middle-income countries, where older vehicle fleets dominate and the adoption of conventional ADAS remains limited. The use of ultrasonic sensing enables reliable obstacle detection in dusty and low-visibility environments, making the system well suited to real-world operating conditions commonly encountered in Ethiopia.

Analytical modeling and system-level simulations demonstrated that the automated braking system significantly improves braking performance by reducing total stopping distance across all tested speeds. An average reduction of 14% was achieved, with improvements of up to 85% at low speeds resulting from the elimination of driver reaction delay. These gains are particularly important in urban and pedestrian-dense environments, where short-range hazard detection and rapid brake actuation are critical. Comparative validation with existing low-cost braking systems confirmed that the proposed approach performs on par with or better than related solutions, while avoiding dependence on modern braking infrastructure such as ABS or ESC, which is often unavailable in legacy vehicles.

Overall, this work provides a practical and scalable pathway for improving vehicle safety in resource-constrained settings, particularly for military, commercial, and rural vehicle fleets. The system’s modular design supports broader deployment across similar vehicle platforms with minimal modification, offering a cost-effective alternative to vehicle replacement or full ADAS integration. Future work will emphasize extended real-world testing under diverse driving and terrain conditions, long-term durability evaluation, and the integration of additional sensing or adaptive control strategies to further enhance reliability, robustness, and real-world applicability.

Footnotes

ORCID iD

Kumlachew Yeneneh

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data will be made available on request.*

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